Subject Areas : Majlesi Journal of Telecommunication Devices
Hassan Mahichi
1
,
Vahid Ghods
2
,
Mohammad Karim Sohrabi
3
,
Arash Sabbaghi
4
1 -
2 -
3 -
4 -
Keywords:
Abstract :
References:
[1] Leonardi M, Martelletti P, Burstein R, Fornari A, Grazzi L, Guekht A, et al. The World Health Organization Intersectoral Global Action Plan on Epilepsy and Other Neurological Disorders and the headache revolution: from headache burden to a global action plan for headache disorders. J Headache Pain 2024;25:4. https://doi.org/10.1186/s10194-023-01700-3.
[2] Zhao H, Li H, Cheng L. Data augmentation for medical image analysis. Biomedical Image Synthesis and Simulation, Elsevier; 2022, p. 279–302. https://doi.org/10.1016/B978-0-12-824349-7.00021-9.
[3] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42. https://doi.org/10.1016/j.media.2017.07.005.
[4] Abu Abeelh E, AbuAbeileh Z. Comparative Effectiveness of Mammography, Ultrasound, and MRI in the Detection of Breast Carcinoma in Dense Breast Tissue: A Systematic Review. Cureus 2024. https://doi.org/10.7759/cureus.59054.
[5] Mahichi H, Ghods V, Sohrabi MK, Sabbaghi A. BreastCNet: Breast Cancer Detection, Classification, and Localization Convolutional Neural Network With Advanced Optimization Techniques. IEEE Access 2025;13:87386–400. https://doi.org/10.1109/ACCESS.2025.3570364.
[6] Arbeláez P, Maire M, Fowlkes C, Malik J. Contour Detection and Hierarchical Image Segmentation. IEEE Trans Pattern Anal Mach Intell 2011;33:898–916. https://doi.org/10.1109/TPAMI.2010.161.
[7] Rainio O, Klén R. Comparison of simple augmentation transformations for a convolutional neural network classifying medical images. Signal Image Video Process 2024;18:3353–60. https://doi.org/10.1007/s11760-024-02998-5.
[8] Aumente-Maestro C, Díez J, Remeseiro B. A multi-task framework for breast cancer segmentation and classification in ultrasound imaging. Comput Methods Programs Biomed 2025;260:108540. https://doi.org/10.1016/j.cmpb.2024.108540.
[9] Petrini DGP, Shimizu C, Roela RA, Valente GV, Folgueira MAAK, Kim HY. Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network. IEEE Access 2022;10:77723–31. https://doi.org/10.1109/ACCESS.2022.3193250.
[10] Kaddes M, Ayid YM, Elshewey AM, Fouad Y. Breast cancer classification based on hybrid CNN with LSTM model. Sci Rep 2025;15:4409. https://doi.org/10.1038/s41598-025-88459-6.
[11] Zaidkilani N, Garcia MA, Puig D. CoAtUNet: A symmetric encoder-decoder with hybrid transformers for semantic segmentation of breast ultrasound images. Neurocomputing 2025;629:129660. https://doi.org/10.1016/j.neucom.2025.129660.
[12] Logan J, Kennedy PJ, Catchpoole D. A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future. Sci Data 2023;10:595. https://doi.org/10.1038/s41597-023-02430-6.
[13] Anand A, Jung S, Lee S. Breast Lesion Detection for Ultrasound Images Using MaskFormer. Sensors 2024;24:6890. https://doi.org/10.3390/s24216890.
[14] Yang K Bin, Lee J, Yang J. Multi-class semantic segmentation of breast tissues from MRI images using U-Net based on Haar wavelet pooling. Sci Rep 2023;13:11704. https://doi.org/10.1038/s41598-023-38557-0.
[15] Chen YA, Grimm LJ, Nedrud M, Rahbar H. MRI characteristics of ductal carcinoma in situ, 2022, p. 145–56. https://doi.org/10.1016/B978-0-12-822729-9.00026-6.
[16] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44. https://doi.org/10.1038/nature14539.
[17] Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. End-to-End Object Detection with Transformers 2020.
[18] Chan H, Hadjiiski LM, Samala RK. Computer‐aided diagnosis in the era of deep learning. Med Phys 2020;47. https://doi.org/10.1002/mp.13764.
[19] Yu Y, Huang J, Wang L, Liang S. A 1D-inception-ResNet based global detection model for thin-skinned multifruit spectral quantitative analysis. Food Control 2025;167:110823. https://doi.org/10.1016/j.foodcont.2024.110823.
[20] Hou Y, Wu Z, Cai X, Zhu T. The application of improved densenet algorithm in accurate image recognition. Sci Rep 2024;14:8645. https://doi.org/10.1038/s41598-024-58421-z.
[21] Qayyum A, Mazher M, Khan T, Razzak I. Semi-supervised 3D-InceptionNet for segmentation and survival prediction of head and neck primary cancers. Eng Appl Artif Intell 2023;117:105590. https://doi.org/10.1016/j.engappai.2022.105590.
[22] Hamnett L, Adewunmi M, Abayomi M, Raheem K, Ahmed F. Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques 2023.
[23] Manigrasso F, Milazzo R, Russo AS, Lamberti F, Strand F, Pagnani A, et al. Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures. Med Image Anal 2025;99:103320. https://doi.org/10.1016/j.media.2024.103320.
[24] Islam MR, Rahman MM, Ali MS, Nafi AAN, Alam MS, Godder TK, et al. Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images. Machine Learning with Applications 2024;16:100555. https://doi.org/10.1016/j.mlwa.2024.100555.
[25] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation 2015.
[26] Wu X, Hong D, Chanussot J. UIU-Net: U-Net in U-Net for Infrared Small Object Detection. IEEE Transactions on Image Processing 2023;32:364–76. https://doi.org/10.1109/TIP.2022.3228497.
[27] Maqsood R, Abid F, Rasheed J, Osman O, Alsubai S. Optimal Res-UNET architecture with deep supervision for tumor segmentation. Front Med (Lausanne) 2025;12. https://doi.org/10.3389/fmed.2025.1593016.
[28] Qin C, Wu Y, Zeng J, Tian L, Zhai Y, Li F, et al. Joint Transformer and Multi-scale CNN for DCE-MRI Breast Cancer Segmentation. Soft Comput 2022;26:8317–34. https://doi.org/10.1007/s00500-022-07235-0.
[29] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation 2015.
[30] Li X, Li M, Yan P, Li G, Jiang Y, Luo H, et al. Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds. International Journal of Network Dynamics and Intelligence 2023:93–116. https://doi.org/10.53941/ijndi0201006.
[31] Jamil S, Jalil Piran Md, Kwon O-J. A Comprehensive Survey of Transformers for Computer Vision. Drones 2023;7:287. https://doi.org/10.3390/drones7050287.
[32] Chen J, Mei J, Li X, Lu Y, Yu Q, Wei Q, et al. TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med Image Anal 2024;97:103280. https://doi.org/10.1016/j.media.2024.103280.
[33] Afrin H, Larson NB, Fatemi M, Alizad A. Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis. Cancers (Basel) 2023;15:3139. https://doi.org/10.3390/cancers15123139.
[34] He Q, Yang Q, Su H, Wang Y. Multi-task learning for segmentation and classification of breast tumors from ultrasound images. Comput Biol Med 2024;173:108319. https://doi.org/10.1016/j.compbiomed.2024.108319.
[35] Zhou Y, Chen H, Li Y, Liu Q, Xu X, Wang S, et al. Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images. Med Image Anal 2021;70:101918. https://doi.org/10.1016/j.media.2020.101918.
[36] Oliver A, Freixenet J, Martí J, Pérez E, Pont J, Denton ERE, et al. A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 2010;14. https://doi.org/10.1016/j.media.2009.12.005.
[37] Shen T, Hao K, Gou C, Wang F-Y. Mass Image Synthesis in Mammogram with Contextual Information Based on GANs. Comput Methods Programs Biomed 2021;202:106019. https://doi.org/10.1016/j.cmpb.2021.106019.
[38] Joshi RC, Singh D, Tiwari V, Dutta MK. An efficient deep neural network based abnormality detection and multi-class breast tumor classification. Multimed Tools Appl 2022;81:13691–711. https://doi.org/10.1007/s11042-021-11240-0.
[39] Rania Maalej, Anis Mezghani. Transfer Learning and Data Augmentation for Improved Breast Cancer Histopathological Images Classifier. International Journal of Computer Information Systems and Industrial Management Applications 2023;15:10.
[40] Carrington AM, Manuel DG, Fieguth PW, Ramsay T, Osmani V, Wernly B, et al. Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation. IEEE Trans Pattern Anal Mach Intell 2023;45:329–41. https://doi.org/10.1109/TPAMI.2022.3145392.
[41] Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 2018;321:321–31. https://doi.org/10.1016/j.neucom.2018.09.013.
[42] S D, S M, S K, S S, R M. GAN based Data Augmentation for Enhanced Tumor Classification. 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP), IEEE; 2020, p. 1–5. https://doi.org/10.1109/ICCCSP49186.2020.9315189.
[43] Shorten C, Khoshgoftaar TM. A survey on Image Data Augmentation for Deep Learning. J Big Data 2019;6:60. https://doi.org/10.1186/s40537-019-0197-0.
[44] Shorten C, Khoshgoftaar TM. A survey on Image Data Augmentation for Deep Learning. J Big Data 2019;6. https://doi.org/10.1186/s40537-019-0197-0.
[45] Scharff Rethfeldt W, McNeilly L, Laasonen M, Meir N, Abutbul-Oz H, Smolander S, et al. Assessment of Developmental Language Disorder in Multilingual Children: Results from an International Survey. Folia Phoniatrica et Logopaedica 2024;76:127–50. https://doi.org/10.1159/000533139.
[46] Rony J, Belharbi S, Dolz J, Ben Ayed I, McCaffrey L, Granger E. Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey. Machine Learning for Biomedical Imaging 2023;2. https://doi.org/10.59275/j.melba.2023-5g54.
[47] Wang C-H, Huang K-Y, Yao Y, Chen J-C, Shuai H-H, Cheng W-H. Lightweight Deep Learning: An Overview. IEEE Consumer Electronics Magazine 2024;13:51–64. https://doi.org/10.1109/MCE.2022.3181759.
[48] Nakach F-Z, Idri A, Goceri E. A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification. Artif Intell Rev 2024;57:327. https://doi.org/10.1007/s10462-024-10984-z.
[49] Iqbal A, Sharif M. BTS-ST: Swin transformer network for segmentation and classification of multimodality breast cancer images. Knowl Based Syst 2023;267:110393. https://doi.org/10.1016/j.knosys.2023.110393.
[50] Yu H, Dai Q. Self-supervised multi-task learning for medical image analysis. Pattern Recognit 2024;150:110327. https://doi.org/10.1016/j.patcog.2024.110327.
[51] Díaz O, Rodríguez-Ruíz A, Sechopoulos I. Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects. Eur J Radiol 2024;175:111457. https://doi.org/10.1016/j.ejrad.2024.111457.
[52] Das HS, Das A, Neog A, Mallik S, Bora K, Zhao Z. Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach. Front Genet 2023;13. https://doi.org/10.3389/fgene.2022.1097207.
[53] Tan M, Pang R, Le Q V. EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020. https://doi.org/10.1109/CVPR42600.2020.01079.
[54] Bochkovskiy A, Wang C-Y, Liao H-YM. YOLOv4: Optimal Speed and Accuracy of Object Detection 2020.
[55] Anand A, Jung S, Lee S. Breast Lesion Detection for Ultrasound Images Using MaskFormer. Sensors 2024;24:6890. https://doi.org/10.3390/s24216890.
[56] Mikhailov N, Shakeel M, Urmanov A, Lee M-H, Demirci MF. Optimization of CNN model for breast cancer classification. 2021 16th International Conference on Electronics Computer and Computation (ICECCO), IEEE; 2021, p. 1–3.
[57] Geras KJ, Mann RM, Moy L. Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives. Radiology 2019;293:246–59. https://doi.org/10.1148/radiol.2019182627.
[58] Hou Y, Gao H, Wang Z, Du C. Improved Grey Wolf Optimization Algorithm and Application. Sensors 2022;22:3810. https://doi.org/10.3390/s22103810.
[59] Lian J, Hui G, Ma L, Zhu T, Wu X, Heidari AA, et al. Parrot optimizer: Algorithm and applications to medical problems. Comput Biol Med 2024;172:108064. https://doi.org/10.1016/j.compbiomed.2024.108064.
[60] Petrini DGP, Shimizu C, Roela RA, Valente GV, Folgueira MAAK, Kim HY. Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network. IEEE Access 2022;10:77723–31. https://doi.org/10.1109/ACCESS.2022.3193250.
[61] Rahman H, Naik Bukht TF, Ahmad R, Almadhor A, Javed AR. Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network. Comput Intell Neurosci 2023;2023:1–11. https://doi.org/10.1155/2023/7717712.
[62] Ayana G, Choe S. BUViTNet: Breast Ultrasound Detection via Vision Transformers. Diagnostics 2022;12:2654. https://doi.org/10.3390/diagnostics12112654.
[63] Prinzi F, Insalaco M, Orlando A, Gaglio S, Vitabile S. A Yolo-Based Model for Breast Cancer Detection in Mammograms. Cognit Comput 2023. https://doi.org/10.1007/s12559-023-10189-6.
[64] Ding W, Wang J, Zhou W, Zhou S, Chang C, Shi J. Joint Localization and Classification of Breast Cancer in B-Mode Ultrasound Imaging via Collaborative Learning With Elastography. IEEE J Biomed Health Inform 2022;26:4474–85. https://doi.org/10.1109/JBHI.2022.3186933.
[65] Yao IZ, Dong M, Hwang WYK. Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions. Mayo Clinic Proceedings: Digital Health 2025:100253. https://doi.org/10.1016/j.mcpdig.2025.100253.
[66] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020;70:7–30. https://doi.org/10.3322/caac.21590.
[67] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394–424. https://doi.org/10.3322/caac.21492.
[68] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer; 2015, p. 234–41.
[69] Redmon J, Farhadi A. YOLOv3: An Incremental Improvement 2018.
[70] He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE; 2017, p. 2980–8. https://doi.org/10.1109/ICCV.2017.322.
[71] Arbeláez P, Maire M, Fowlkes C, Malik J. Contour Detection and Hierarchical Image Segmentation. IEEE Trans Pattern Anal Mach Intell 2011;33:898–916. https://doi.org/10.1109/TPAMI.2010.161.
[72] Boykov Y, Funka-Lea G. Graph Cuts and Efficient N-D Image Segmentation. Int J Comput Vis 2006;70:109–31. https://doi.org/10.1007/s11263-006-7934-5.
[73] Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans Pattern Anal Mach Intell 2018;40:834–48. https://doi.org/10.1109/TPAMI.2017.2699184.
[74] He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE; 2017, p. 2980–8. https://doi.org/10.1109/ICCV.2017.322.
[75] Liu M, Yao D, Liu Z, Guo J, Chen J. An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent. Comput Intell Neurosci 2023;2023. https://doi.org/10.1155/2023/4765891.
[76] Umer MJ, Sharif MI, Kim J. Breast Cancer Segmentation From Ultrasound Images Using Multiscale Cascaded Convolution With Residual Attention-Based Double Decoder Network. IEEE Access 2024;12:107888–902. https://doi.org/10.1109/ACCESS.2024.3429386.
[77] Zhang S, Liao M, Wang J, Zhu Y, Zhang Y, Zhang J, et al. Fully automatic tumor segmentation of breast ultrasound images with deep learning. J Appl Clin Med Phys 2023;24. https://doi.org/10.1002/acm2.13863.
[78] Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016- December, 2016. https://doi.org/10.1109/CVPR.2016.91.
[79] Goudarzi S, Whyte J, Boily M, Towers A, Kilgour RD, Rivaz H. Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm. IEEE Trans Biomed Eng 2023;70:2552–63. https://doi.org/10.1109/TBME.2023.3253646.
[80] Spanhol FA, Oliveira LS, Petitjean C, Heutte L. A Dataset for Breast Cancer Histopathological Image Classification. IEEE Trans Biomed Eng 2016;63:1455–62. https://doi.org/10.1109/TBME.2015.2496264.
[81] University of Nottingham. Nottingham Prognostic Index (NPI) dataset. https://www.nottingham.ac.uk/pathology/protocols/npi/npi.aspx. Accessed May 12, 2023. n.d.
[82] Tafavvoghi M, Bongo LA, Shvetsov N, Busund L-TR, Møllersen K. Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review. J Pathol Inform 2024;15:100363. https://doi.org/10.1016/j.jpi.2024.100363.
[83] University of California Irvine Machine Learning Repository. Wisconsin Diagnostic Breast Cancer (WDBC) dataset. https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic). Accessed May 12, 2023. n.d.
[84] Soltani H, Amroune M, Bendib I, Haouam M-Y, Benkhelifa E, Fraz MM. Breast lesions segmentation and classification in a two-stage process based on Mask-RCNN and Transfer Learning. Multimed Tools Appl 2023:1–18.
[85] Khan FS, Mohd MNH, Khan MD, Bagchi S. Breast Cancer Histological Images Nuclei Segmentation using Mask Regional Convolutional Neural Network. 2020 IEEE Student Conference on Research and Development (SCOReD), IEEE; 2020, p. 1–6. https://doi.org/10.1109/SCOReD50371.2020.9383186.
[86] Bhatti HMA, Li J, Siddeeq S, Rehman A, Manzoor A. Multi-detection and Segmentation of Breast Lesions Based on Mask RCNN-FPN. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE; 2020, p. 2698–704. https://doi.org/10.1109/BIBM49941.2020.9313170.
[87] Zhang Y, Chan S, Park VY, Chang K-T, Mehta S, Kim MJ, et al. Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images. Acad Radiol 2022;29:S135–44. https://doi.org/10.1016/j.acra.2020.12.001.
[88] Karunanayake N, Makhanov SS. When deep learning is not enough: artificial life as a supplementary tool for segmentation of ultrasound images of breast cancer. Med Biol Eng Comput 2024. https://doi.org/10.1007/s11517-024-03026-x.
[89] Abdelhafiz D, Bi J, Ammar R, Yang C, Nabavi S. Convolutional neural network for automated mass segmentation in mammography. BMC Bioinformatics 2020;21:192. https://doi.org/10.1186/s12859-020-3521-y.
[90] Baccouche A, Garcia-Zapirain B, Castillo Olea C, Elmaghraby AS. Connected-UNets: a deep learning architecture for breast mass segmentation. NPJ Breast Cancer 2021;7:151. https://doi.org/10.1038/s41523-021-00358-x.
[91] Siriapisith T, Kusakunniran W, Haddawy P. Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation. Comput Biol Med 2020;126:103997. https://doi.org/10.1016/j.compbiomed.2020.103997.
[92] Chen H, Qi X, Yu L, Heng P-A. DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE; 2016, p. 2487–96. https://doi.org/10.1109/CVPR.2016.273.
[93] Coupeau P, Fasquel J-B, Dinomais M. On the use of GNN-based structural information to improve CNN-based semantic image segmentation. J Vis Commun Image Represent 2024;101:104167. https://doi.org/10.1016/j.jvcir.2024.104167.
[94] Gao Y, Liu B, Zhu Y, Chen L, Tan M, Xiao X, et al. Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy. Quant Imaging Med Surg 2021;11:2265–78. https://doi.org/10.21037/qims-20-12B.
[95] Dafni Rose J, VijayaKumar K, Singh L, Sharma SK. Computer-aided diagnosis for breast cancer detection and classification using optimal region growing segmentation with MobileNet model. Concurrent Engineering 2022;30:181–9. https://doi.org/10.1177/1063293X221080518.
[96] Aumente-Maestro C, Díez J, Remeseiro B. A multi-task framework for breast cancer segmentation and classification in ultrasound imaging. Comput Methods Programs Biomed 2025;260:108540. https://doi.org/10.1016/j.cmpb.2024.108540.
[97] Aumente-Maestro C, Díez J, Remeseiro B. A multi-task framework for breast cancer segmentation and classification in ultrasound imaging. Comput Methods Programs Biomed 2025;260:108540. https://doi.org/10.1016/j.cmpb.2024.108540.
[98] Cheng Z, Liu M, Yan C, Wang S. Dynamic domain generalization for medical image segmentation. Neural Networks 2025;184:107073. https://doi.org/10.1016/j.neunet.2024.107073.
[99] Aslam M. Cochran’s Q test for analyzing categorical data under uncertainty. J Big Data 2023;10:147. https://doi.org/10.1186/s40537-023-00823-3.
[100] Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998;86:2278–324. https://doi.org/10.1109/5.726791.
[101] Hekal AA, Elnakib A, Moustafa HE-D, Amer HM. Breast Cancer Segmentation From Ultrasound Images Using Deep Dual-Decoder Technology With Attention Network. IEEE Access 2024;12:10087–101. https://doi.org/10.1109/ACCESS.2024.3351564.
[102] Alam T, Shia W-C, Hsu F-R, Hassan T. Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework. Biomedicines 2023;11:1536. https://doi.org/10.3390/biomedicines11061536.
[103] Muduli D, Dash R, Majhi B. Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach. Biomed Signal Process Control 2022;71:102825. https://doi.org/10.1016/j.bspc.2021.102825.
[104] Thomas C, Byra M, Marti R, Yap MH, Zwiggelaar R. BUS‐Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets. Med Phys 2023.
[105] Das A, Rana S. Exploring Residual Networks for Breast Cancer Detection from Ultrasound Images. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE; 2021, p. 1–6. https://doi.org/10.1109/ICCCNT51525.2021.9580160.
[106] Huynh HN, Tran AT, Tran TN. Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms. Applied Sciences 2023;13:6894. https://doi.org/10.3390/app13126894.
[107] Sahu A, Das PK, Meher S. An automatic sparse-based deep cascade framework with multilayer representation for detecting breast cancer. Measurement 2024;228:114375. https://doi.org/10.1016/j.measurement.2024.114375.
[108] Kumar P, Srivastava S, Mishra RK, Sai YP. End-to-end improved convolutional neural network model for breast cancer detection using mammographic data. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 2022;19:375–84. https://doi.org/10.1177/1548512920973268.
[109] Sait ARW, Nagaraj R. An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images. Diagnostics 2024;14:227. https://doi.org/10.3390/diagnostics14020227.
[110] Mishra AK, Roy P, Bandyopadhyay S, Das SK. A multi‐task learning based approach for efficient breast cancer detection and classification. Expert Syst 2022;39. https://doi.org/10.1111/exsy.13047.
[111] Vogl W-D, Pinker K, Helbich TH, Bickel H, Grabner G, Bogner W, et al. Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features. Eur Radiol Exp 2019;3:18. https://doi.org/10.1186/s41747-019-0096-3.
[112] Vakanski A, Xian M, Freer PE. Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images. Ultrasound Med Biol 2020;46:2819–33. https://doi.org/10.1016/j.ultrasmedbio.2020.06.015.
[113] Irfan R, Almazroi AA, Rauf HT, Damaševičius R, Nasr EA, Abdelgawad AE. Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion. Diagnostics 2021;11:1212. https://doi.org/10.3390/diagnostics11071212.
[114] Zhao T, Dai H. Breast Tumor Ultrasound Image Segmentation Method Based on Improved Residual U-Net Network. Comput Intell Neurosci 2022;2022:1–9. https://doi.org/10.1155/2022/3905998.
[115] Bobowicz M, Badocha M, Gwozdziewicz K, Rygusik M, Kalinowska P, Szurowska E, et al. Segmentation-based BI-RADS ensemble classification of breast tumours in ultrasound images. Int J Med Inform 2024;189:105522. https://doi.org/10.1016/j.ijmedinf.2024.105522.
[116] Hurtik P, Molek V, Hula J, Vajgl M, Vlasanek P, Nejezchleba T. Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3 2020.
[117] Ma Y, Peng Y. Mammogram mass segmentation and classification based on cross-view VAE and spatial hidden factor disentanglement. Phys Eng Sci Med 2024;47:223–38. https://doi.org/10.1007/s13246-023-01359-9.
[118] Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics 2024;14:848. https://doi.org/10.3390/diagnostics14080848.
[119] Pramanik P, Roy A, Cuevas E, Perez-Cisneros M, Sarkar R. DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images. PLoS One 2024;19:e0303670. https://doi.org/10.1371/journal.pone.0303670.
[120] Madhu G, Meher Bonasi A, Kautish S, Almazyad AS, Mohamed AW, Werner F, et al. UCapsNet: A Two-Stage Deep Learning Model Using U-Net and Capsule Network for Breast Cancer Segmentation and Classification in Ultrasound Imaging. Cancers (Basel) 2024;16:3777. https://doi.org/10.3390/cancers16223777.
[121] Schutte S, Uddin J. Deep Segmentation Techniques for Breast Cancer Diagnosis. BioMedInformatics 2024;4:921–45. https://doi.org/10.3390/biomedinformatics4020052.
[122] Afrin H, Larson NB, Fatemi M, Alizad A. Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis. Cancers (Basel) 2023;15:3139. https://doi.org/10.3390/cancers15123139.
[123] Huang Z, Zhang X, Ju Y, Zhang G, Chang W, Song H, et al. Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound. Insights Imaging 2024;15:227. https://doi.org/10.1186/s13244-024-01810-9.
[124] Peta J, Koppu S. Explainable Soft Attentive EfficientNet for breast cancer classification in histopathological images. Biomed Signal Process Control 2024;90:105828. https://doi.org/10.1016/j.bspc.2023.105828.
[125] Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H. Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification 2018.
[126] He K, Gkioxari G, Dollár P, Girshick R. Mask r-cnn. Proceedings of the IEEE international conference on computer vision, 2017, p. 2961–9.
[127] Zhang Y, Chan S, Park VY, Chang K-T, Mehta S, Kim MJ, et al. Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images. Acad Radiol 2022;29:S135–44. https://doi.org/10.1016/j.acra.2020.12.001.
[128] Irfan R, Almazroi AA, Rauf HT, Damaševičius R, Nasr EA, Abdelgawad AE. Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion. Diagnostics 2021;11:1212. https://doi.org/10.3390/diagnostics11071212.
[129] Di Salvo F, Doerrich S, Ledig C. MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions 2024.
[1] Leonardi M, Martelletti P, Burstein R, Fornari A, Grazzi L, Guekht A, et al. The World Health Organization Intersectoral Global Action Plan on Epilepsy and Other Neurological Disorders and the headache revolution: from headache burden to a global action plan for headache disorders. J Headache Pain 2024;25:4. https://doi.org/10.1186/s10194-023-01700-3.
[2] Zhao H, Li H, Cheng L. Data augmentation for medical image analysis. Biomedical Image Synthesis and Simulation, Elsevier; 2022, p. 279–302. https://doi.org/10.1016/B978-0-12-824349-7.00021-9.
[3] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42. https://doi.org/10.1016/j.media.2017.07.005.
[4] Abu Abeelh E, AbuAbeileh Z. Comparative Effectiveness of Mammography, Ultrasound, and MRI in the Detection of Breast Carcinoma in Dense Breast Tissue: A Systematic Review. Cureus 2024. https://doi.org/10.7759/cureus.59054.
[5] Mahichi H, Ghods V, Sohrabi MK, Sabbaghi A. BreastCNet: Breast Cancer Detection, Classification, and Localization Convolutional Neural Network With Advanced Optimization Techniques. IEEE Access 2025;13:87386–400. https://doi.org/10.1109/ACCESS.2025.3570364.
[6] Arbeláez P, Maire M, Fowlkes C, Malik J. Contour Detection and Hierarchical Image Segmentation. IEEE Trans Pattern Anal Mach Intell 2011;33:898–916. https://doi.org/10.1109/TPAMI.2010.161.
[7] Rainio O, Klén R. Comparison of simple augmentation transformations for a convolutional neural network classifying medical images. Signal Image Video Process 2024;18:3353–60. https://doi.org/10.1007/s11760-024-02998-5.
[8] Aumente-Maestro C, Díez J, Remeseiro B. A multi-task framework for breast cancer segmentation and classification in ultrasound imaging. Comput Methods Programs Biomed 2025;260:108540. https://doi.org/10.1016/j.cmpb.2024.108540.
[9] Petrini DGP, Shimizu C, Roela RA, Valente GV, Folgueira MAAK, Kim HY. Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network. IEEE Access 2022;10:77723–31. https://doi.org/10.1109/ACCESS.2022.3193250.
[10] Kaddes M, Ayid YM, Elshewey AM, Fouad Y. Breast cancer classification based on hybrid CNN with LSTM model. Sci Rep 2025;15:4409. https://doi.org/10.1038/s41598-025-88459-6.
[11] Zaidkilani N, Garcia MA, Puig D. CoAtUNet: A symmetric encoder-decoder with hybrid transformers for semantic segmentation of breast ultrasound images. Neurocomputing 2025;629:129660. https://doi.org/10.1016/j.neucom.2025.129660.
[12] Logan J, Kennedy PJ, Catchpoole D. A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future. Sci Data 2023;10:595. https://doi.org/10.1038/s41597-023-02430-6.
[13] Anand A, Jung S, Lee S. Breast Lesion Detection for Ultrasound Images Using MaskFormer. Sensors 2024;24:6890. https://doi.org/10.3390/s24216890.
[14] Yang K Bin, Lee J, Yang J. Multi-class semantic segmentation of breast tissues from MRI images using U-Net based on Haar wavelet pooling. Sci Rep 2023;13:11704. https://doi.org/10.1038/s41598-023-38557-0.
[15] Chen YA, Grimm LJ, Nedrud M, Rahbar H. MRI characteristics of ductal carcinoma in situ, 2022, p. 145–56. https://doi.org/10.1016/B978-0-12-822729-9.00026-6.
[16] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44. https://doi.org/10.1038/nature14539.
[17] Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. End-to-End Object Detection with Transformers 2020.
[18] Chan H, Hadjiiski LM, Samala RK. Computer‐aided diagnosis in the era of deep learning. Med Phys 2020;47. https://doi.org/10.1002/mp.13764.
[19] Yu Y, Huang J, Wang L, Liang S. A 1D-inception-ResNet based global detection model for thin-skinned multifruit spectral quantitative analysis. Food Control 2025;167:110823. https://doi.org/10.1016/j.foodcont.2024.110823.
[20] Hou Y, Wu Z, Cai X, Zhu T. The application of improved densenet algorithm in accurate image recognition. Sci Rep 2024;14:8645. https://doi.org/10.1038/s41598-024-58421-z.
[21] Qayyum A, Mazher M, Khan T, Razzak I. Semi-supervised 3D-InceptionNet for segmentation and survival prediction of head and neck primary cancers. Eng Appl Artif Intell 2023;117:105590. https://doi.org/10.1016/j.engappai.2022.105590.
[22] Hamnett L, Adewunmi M, Abayomi M, Raheem K, Ahmed F. Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques 2023.
[23] Manigrasso F, Milazzo R, Russo AS, Lamberti F, Strand F, Pagnani A, et al. Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures. Med Image Anal 2025;99:103320. https://doi.org/10.1016/j.media.2024.103320.
[24] Islam MR, Rahman MM, Ali MS, Nafi AAN, Alam MS, Godder TK, et al. Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images. Machine Learning with Applications 2024;16:100555. https://doi.org/10.1016/j.mlwa.2024.100555.
[25] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation 2015.
[26] Wu X, Hong D, Chanussot J. UIU-Net: U-Net in U-Net for Infrared Small Object Detection. IEEE Transactions on Image Processing 2023;32:364–76. https://doi.org/10.1109/TIP.2022.3228497.
[27] Maqsood R, Abid F, Rasheed J, Osman O, Alsubai S. Optimal Res-UNET architecture with deep supervision for tumor segmentation. Front Med (Lausanne) 2025;12. https://doi.org/10.3389/fmed.2025.1593016.
[28] Qin C, Wu Y, Zeng J, Tian L, Zhai Y, Li F, et al. Joint Transformer and Multi-scale CNN for DCE-MRI Breast Cancer Segmentation. Soft Comput 2022;26:8317–34. https://doi.org/10.1007/s00500-022-07235-0.
[29] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation 2015.
[30] Li X, Li M, Yan P, Li G, Jiang Y, Luo H, et al. Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds. International Journal of Network Dynamics and Intelligence 2023:93–116. https://doi.org/10.53941/ijndi0201006.
[31] Jamil S, Jalil Piran Md, Kwon O-J. A Comprehensive Survey of Transformers for Computer Vision. Drones 2023;7:287. https://doi.org/10.3390/drones7050287.
[32] Chen J, Mei J, Li X, Lu Y, Yu Q, Wei Q, et al. TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med Image Anal 2024;97:103280. https://doi.org/10.1016/j.media.2024.103280.
[33] Afrin H, Larson NB, Fatemi M, Alizad A. Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis. Cancers (Basel) 2023;15:3139. https://doi.org/10.3390/cancers15123139.
[34] He Q, Yang Q, Su H, Wang Y. Multi-task learning for segmentation and classification of breast tumors from ultrasound images. Comput Biol Med 2024;173:108319. https://doi.org/10.1016/j.compbiomed.2024.108319.
[35] Zhou Y, Chen H, Li Y, Liu Q, Xu X, Wang S, et al. Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images. Med Image Anal 2021;70:101918. https://doi.org/10.1016/j.media.2020.101918.
[36] Oliver A, Freixenet J, Martí J, Pérez E, Pont J, Denton ERE, et al. A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 2010;14. https://doi.org/10.1016/j.media.2009.12.005.
[37] Shen T, Hao K, Gou C, Wang F-Y. Mass Image Synthesis in Mammogram with Contextual Information Based on GANs. Comput Methods Programs Biomed 2021;202:106019. https://doi.org/10.1016/j.cmpb.2021.106019.
[38] Joshi RC, Singh D, Tiwari V, Dutta MK. An efficient deep neural network based abnormality detection and multi-class breast tumor classification. Multimed Tools Appl 2022;81:13691–711. https://doi.org/10.1007/s11042-021-11240-0.
[39] Rania Maalej, Anis Mezghani. Transfer Learning and Data Augmentation for Improved Breast Cancer Histopathological Images Classifier. International Journal of Computer Information Systems and Industrial Management Applications 2023;15:10.
[40] Carrington AM, Manuel DG, Fieguth PW, Ramsay T, Osmani V, Wernly B, et al. Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation. IEEE Trans Pattern Anal Mach Intell 2023;45:329–41. https://doi.org/10.1109/TPAMI.2022.3145392.
[41] Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 2018;321:321–31. https://doi.org/10.1016/j.neucom.2018.09.013.
[42] S D, S M, S K, S S, R M. GAN based Data Augmentation for Enhanced Tumor Classification. 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP), IEEE; 2020, p. 1–5. https://doi.org/10.1109/ICCCSP49186.2020.9315189.
[43] Shorten C, Khoshgoftaar TM. A survey on Image Data Augmentation for Deep Learning. J Big Data 2019;6:60. https://doi.org/10.1186/s40537-019-0197-0.
[44] Shorten C, Khoshgoftaar TM. A survey on Image Data Augmentation for Deep Learning. J Big Data 2019;6. https://doi.org/10.1186/s40537-019-0197-0.
[45] Scharff Rethfeldt W, McNeilly L, Laasonen M, Meir N, Abutbul-Oz H, Smolander S, et al. Assessment of Developmental Language Disorder in Multilingual Children: Results from an International Survey. Folia Phoniatrica et Logopaedica 2024;76:127–50. https://doi.org/10.1159/000533139.
[46] Rony J, Belharbi S, Dolz J, Ben Ayed I, McCaffrey L, Granger E. Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey. Machine Learning for Biomedical Imaging 2023;2. https://doi.org/10.59275/j.melba.2023-5g54.
[47] Wang C-H, Huang K-Y, Yao Y, Chen J-C, Shuai H-H, Cheng W-H. Lightweight Deep Learning: An Overview. IEEE Consumer Electronics Magazine 2024;13:51–64. https://doi.org/10.1109/MCE.2022.3181759.
[48] Nakach F-Z, Idri A, Goceri E. A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification. Artif Intell Rev 2024;57:327. https://doi.org/10.1007/s10462-024-10984-z.
[49] Iqbal A, Sharif M. BTS-ST: Swin transformer network for segmentation and classification of multimodality breast cancer images. Knowl Based Syst 2023;267:110393. https://doi.org/10.1016/j.knosys.2023.110393.
[50] Yu H, Dai Q. Self-supervised multi-task learning for medical image analysis. Pattern Recognit 2024;150:110327. https://doi.org/10.1016/j.patcog.2024.110327.
[51] Díaz O, Rodríguez-Ruíz A, Sechopoulos I. Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects. Eur J Radiol 2024;175:111457. https://doi.org/10.1016/j.ejrad.2024.111457.
[52] Das HS, Das A, Neog A, Mallik S, Bora K, Zhao Z. Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach. Front Genet 2023;13. https://doi.org/10.3389/fgene.2022.1097207.
[53] Tan M, Pang R, Le Q V. EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020. https://doi.org/10.1109/CVPR42600.2020.01079.
[54] Bochkovskiy A, Wang C-Y, Liao H-YM. YOLOv4: Optimal Speed and Accuracy of Object Detection 2020.
[55] Anand A, Jung S, Lee S. Breast Lesion Detection for Ultrasound Images Using MaskFormer. Sensors 2024;24:6890. https://doi.org/10.3390/s24216890.
[56] Mikhailov N, Shakeel M, Urmanov A, Lee M-H, Demirci MF. Optimization of CNN model for breast cancer classification. 2021 16th International Conference on Electronics Computer and Computation (ICECCO), IEEE; 2021, p. 1–3.
[57] Geras KJ, Mann RM, Moy L. Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives. Radiology 2019;293:246–59. https://doi.org/10.1148/radiol.2019182627.
[58] Hou Y, Gao H, Wang Z, Du C. Improved Grey Wolf Optimization Algorithm and Application. Sensors 2022;22:3810. https://doi.org/10.3390/s22103810.
[59] Lian J, Hui G, Ma L, Zhu T, Wu X, Heidari AA, et al. Parrot optimizer: Algorithm and applications to medical problems. Comput Biol Med 2024;172:108064. https://doi.org/10.1016/j.compbiomed.2024.108064.
[60] Petrini DGP, Shimizu C, Roela RA, Valente GV, Folgueira MAAK, Kim HY. Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network. IEEE Access 2022;10:77723–31. https://doi.org/10.1109/ACCESS.2022.3193250.
[61] Rahman H, Naik Bukht TF, Ahmad R, Almadhor A, Javed AR. Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network. Comput Intell Neurosci 2023;2023:1–11. https://doi.org/10.1155/2023/7717712.
[62] Ayana G, Choe S. BUViTNet: Breast Ultrasound Detection via Vision Transformers. Diagnostics 2022;12:2654. https://doi.org/10.3390/diagnostics12112654.
[63] Prinzi F, Insalaco M, Orlando A, Gaglio S, Vitabile S. A Yolo-Based Model for Breast Cancer Detection in Mammograms. Cognit Comput 2023. https://doi.org/10.1007/s12559-023-10189-6.
[64] Ding W, Wang J, Zhou W, Zhou S, Chang C, Shi J. Joint Localization and Classification of Breast Cancer in B-Mode Ultrasound Imaging via Collaborative Learning With Elastography. IEEE J Biomed Health Inform 2022;26:4474–85. https://doi.org/10.1109/JBHI.2022.3186933.
[65] Yao IZ, Dong M, Hwang WYK. Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions. Mayo Clinic Proceedings: Digital Health 2025:100253. https://doi.org/10.1016/j.mcpdig.2025.100253.
[66] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020;70:7–30. https://doi.org/10.3322/caac.21590.
[67] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394–424. https://doi.org/10.3322/caac.21492.
[68] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer; 2015, p. 234–41.
[69] Redmon J, Farhadi A. YOLOv3: An Incremental Improvement 2018.
[70] He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE; 2017, p. 2980–8. https://doi.org/10.1109/ICCV.2017.322.
[71] Arbeláez P, Maire M, Fowlkes C, Malik J. Contour Detection and Hierarchical Image Segmentation. IEEE Trans Pattern Anal Mach Intell 2011;33:898–916. https://doi.org/10.1109/TPAMI.2010.161.
[72] Boykov Y, Funka-Lea G. Graph Cuts and Efficient N-D Image Segmentation. Int J Comput Vis 2006;70:109–31. https://doi.org/10.1007/s11263-006-7934-5.
[73] Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans Pattern Anal Mach Intell 2018;40:834–48. https://doi.org/10.1109/TPAMI.2017.2699184.
[74] He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE; 2017, p. 2980–8. https://doi.org/10.1109/ICCV.2017.322.
[75] Liu M, Yao D, Liu Z, Guo J, Chen J. An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent. Comput Intell Neurosci 2023;2023. https://doi.org/10.1155/2023/4765891.
[76] Umer MJ, Sharif MI, Kim J. Breast Cancer Segmentation From Ultrasound Images Using Multiscale Cascaded Convolution With Residual Attention-Based Double Decoder Network. IEEE Access 2024;12:107888–902. https://doi.org/10.1109/ACCESS.2024.3429386.
[77] Zhang S, Liao M, Wang J, Zhu Y, Zhang Y, Zhang J, et al. Fully automatic tumor segmentation of breast ultrasound images with deep learning. J Appl Clin Med Phys 2023;24. https://doi.org/10.1002/acm2.13863.
[78] Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016- December, 2016. https://doi.org/10.1109/CVPR.2016.91.
[79] Goudarzi S, Whyte J, Boily M, Towers A, Kilgour RD, Rivaz H. Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm. IEEE Trans Biomed Eng 2023;70:2552–63. https://doi.org/10.1109/TBME.2023.3253646.
[80] Spanhol FA, Oliveira LS, Petitjean C, Heutte L. A Dataset for Breast Cancer Histopathological Image Classification. IEEE Trans Biomed Eng 2016;63:1455–62. https://doi.org/10.1109/TBME.2015.2496264.
[81] University of Nottingham. Nottingham Prognostic Index (NPI) dataset. https://www.nottingham.ac.uk/pathology/protocols/npi/npi.aspx. Accessed May 12, 2023. n.d.
[82] Tafavvoghi M, Bongo LA, Shvetsov N, Busund L-TR, Møllersen K. Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review. J Pathol Inform 2024;15:100363. https://doi.org/10.1016/j.jpi.2024.100363.
[83] University of California Irvine Machine Learning Repository. Wisconsin Diagnostic Breast Cancer (WDBC) dataset. https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic). Accessed May 12, 2023. n.d.
[84] Soltani H, Amroune M, Bendib I, Haouam M-Y, Benkhelifa E, Fraz MM. Breast lesions segmentation and classification in a two-stage process based on Mask-RCNN and Transfer Learning. Multimed Tools Appl 2023:1–18.
[85] Khan FS, Mohd MNH, Khan MD, Bagchi S. Breast Cancer Histological Images Nuclei Segmentation using Mask Regional Convolutional Neural Network. 2020 IEEE Student Conference on Research and Development (SCOReD), IEEE; 2020, p. 1–6. https://doi.org/10.1109/SCOReD50371.2020.9383186.
[86] Bhatti HMA, Li J, Siddeeq S, Rehman A, Manzoor A. Multi-detection and Segmentation of Breast Lesions Based on Mask RCNN-FPN. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE; 2020, p. 2698–704. https://doi.org/10.1109/BIBM49941.2020.9313170.
[87] Zhang Y, Chan S, Park VY, Chang K-T, Mehta S, Kim MJ, et al. Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images. Acad Radiol 2022;29:S135–44. https://doi.org/10.1016/j.acra.2020.12.001.
[88] Karunanayake N, Makhanov SS. When deep learning is not enough: artificial life as a supplementary tool for segmentation of ultrasound images of breast cancer. Med Biol Eng Comput 2024. https://doi.org/10.1007/s11517-024-03026-x.
[89] Abdelhafiz D, Bi J, Ammar R, Yang C, Nabavi S. Convolutional neural network for automated mass segmentation in mammography. BMC Bioinformatics 2020;21:192. https://doi.org/10.1186/s12859-020-3521-y.
[90] Baccouche A, Garcia-Zapirain B, Castillo Olea C, Elmaghraby AS. Connected-UNets: a deep learning architecture for breast mass segmentation. NPJ Breast Cancer 2021;7:151. https://doi.org/10.1038/s41523-021-00358-x.
[91] Siriapisith T, Kusakunniran W, Haddawy P. Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation. Comput Biol Med 2020;126:103997. https://doi.org/10.1016/j.compbiomed.2020.103997.
[92] Chen H, Qi X, Yu L, Heng P-A. DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE; 2016, p. 2487–96. https://doi.org/10.1109/CVPR.2016.273.
[93] Coupeau P, Fasquel J-B, Dinomais M. On the use of GNN-based structural information to improve CNN-based semantic image segmentation. J Vis Commun Image Represent 2024;101:104167. https://doi.org/10.1016/j.jvcir.2024.104167.
[94] Gao Y, Liu B, Zhu Y, Chen L, Tan M, Xiao X, et al. Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy. Quant Imaging Med Surg 2021;11:2265–78. https://doi.org/10.21037/qims-20-12B.
[95] Dafni Rose J, VijayaKumar K, Singh L, Sharma SK. Computer-aided diagnosis for breast cancer detection and classification using optimal region growing segmentation with MobileNet model. Concurrent Engineering 2022;30:181–9. https://doi.org/10.1177/1063293X221080518.
[96] Aumente-Maestro C, Díez J, Remeseiro B. A multi-task framework for breast cancer segmentation and classification in ultrasound imaging. Comput Methods Programs Biomed 2025;260:108540. https://doi.org/10.1016/j.cmpb.2024.108540.
[97] Aumente-Maestro C, Díez J, Remeseiro B. A multi-task framework for breast cancer segmentation and classification in ultrasound imaging. Comput Methods Programs Biomed 2025;260:108540. https://doi.org/10.1016/j.cmpb.2024.108540.
[98] Cheng Z, Liu M, Yan C, Wang S. Dynamic domain generalization for medical image segmentation. Neural Networks 2025;184:107073. https://doi.org/10.1016/j.neunet.2024.107073.
[99] Aslam M. Cochran’s Q test for analyzing categorical data under uncertainty. J Big Data 2023;10:147. https://doi.org/10.1186/s40537-023-00823-3.
[100] Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998;86:2278–324. https://doi.org/10.1109/5.726791.
[101] Hekal AA, Elnakib A, Moustafa HE-D, Amer HM. Breast Cancer Segmentation From Ultrasound Images Using Deep Dual-Decoder Technology With Attention Network. IEEE Access 2024;12:10087–101. https://doi.org/10.1109/ACCESS.2024.3351564.
[102] Alam T, Shia W-C, Hsu F-R, Hassan T. Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework. Biomedicines 2023;11:1536. https://doi.org/10.3390/biomedicines11061536.
[103] Muduli D, Dash R, Majhi B. Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach. Biomed Signal Process Control 2022;71:102825. https://doi.org/10.1016/j.bspc.2021.102825.
[104] Thomas C, Byra M, Marti R, Yap MH, Zwiggelaar R. BUS‐Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets. Med Phys 2023.
[105] Das A, Rana S. Exploring Residual Networks for Breast Cancer Detection from Ultrasound Images. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE; 2021, p. 1–6. https://doi.org/10.1109/ICCCNT51525.2021.9580160.
[106] Huynh HN, Tran AT, Tran TN. Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms. Applied Sciences 2023;13:6894. https://doi.org/10.3390/app13126894.
[107] Sahu A, Das PK, Meher S. An automatic sparse-based deep cascade framework with multilayer representation for detecting breast cancer. Measurement 2024;228:114375. https://doi.org/10.1016/j.measurement.2024.114375.
[108] Kumar P, Srivastava S, Mishra RK, Sai YP. End-to-end improved convolutional neural network model for breast cancer detection using mammographic data. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 2022;19:375–84. https://doi.org/10.1177/1548512920973268.
[109] Sait ARW, Nagaraj R. An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images. Diagnostics 2024;14:227. https://doi.org/10.3390/diagnostics14020227.
[110] Mishra AK, Roy P, Bandyopadhyay S, Das SK. A multi‐task learning based approach for efficient breast cancer detection and classification. Expert Syst 2022;39. https://doi.org/10.1111/exsy.13047.
[111] Vogl W-D, Pinker K, Helbich TH, Bickel H, Grabner G, Bogner W, et al. Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features. Eur Radiol Exp 2019;3:18. https://doi.org/10.1186/s41747-019-0096-3.
[112] Vakanski A, Xian M, Freer PE. Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images. Ultrasound Med Biol 2020;46:2819–33. https://doi.org/10.1016/j.ultrasmedbio.2020.06.015.
[113] Irfan R, Almazroi AA, Rauf HT, Damaševičius R, Nasr EA, Abdelgawad AE. Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion. Diagnostics 2021;11:1212. https://doi.org/10.3390/diagnostics11071212.
[114] Zhao T, Dai H. Breast Tumor Ultrasound Image Segmentation Method Based on Improved Residual U-Net Network. Comput Intell Neurosci 2022;2022:1–9. https://doi.org/10.1155/2022/3905998.
[115] Bobowicz M, Badocha M, Gwozdziewicz K, Rygusik M, Kalinowska P, Szurowska E, et al. Segmentation-based BI-RADS ensemble classification of breast tumours in ultrasound images. Int J Med Inform 2024;189:105522. https://doi.org/10.1016/j.ijmedinf.2024.105522.
[116] Hurtik P, Molek V, Hula J, Vajgl M, Vlasanek P, Nejezchleba T. Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3 2020.
[117] Ma Y, Peng Y. Mammogram mass segmentation and classification based on cross-view VAE and spatial hidden factor disentanglement. Phys Eng Sci Med 2024;47:223–38. https://doi.org/10.1007/s13246-023-01359-9.
[118] Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics 2024;14:848. https://doi.org/10.3390/diagnostics14080848.
[119] Pramanik P, Roy A, Cuevas E, Perez-Cisneros M, Sarkar R. DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images. PLoS One 2024;19:e0303670. https://doi.org/10.1371/journal.pone.0303670.
[120] Madhu G, Meher Bonasi A, Kautish S, Almazyad AS, Mohamed AW, Werner F, et al. UCapsNet: A Two-Stage Deep Learning Model Using U-Net and Capsule Network for Breast Cancer Segmentation and Classification in Ultrasound Imaging. Cancers (Basel) 2024;16:3777. https://doi.org/10.3390/cancers16223777.
[121] Schutte S, Uddin J. Deep Segmentation Techniques for Breast Cancer Diagnosis. BioMedInformatics 2024;4:921–45. https://doi.org/10.3390/biomedinformatics4020052.
[122] Afrin H, Larson NB, Fatemi M, Alizad A. Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis. Cancers (Basel) 2023;15:3139. https://doi.org/10.3390/cancers15123139.
[123] Huang Z, Zhang X, Ju Y, Zhang G, Chang W, Song H, et al. Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound. Insights Imaging 2024;15:227. https://doi.org/10.1186/s13244-024-01810-9.
[124] Peta J, Koppu S. Explainable Soft Attentive EfficientNet for breast cancer classification in histopathological images. Biomed Signal Process Control 2024;90:105828. https://doi.org/10.1016/j.bspc.2023.105828.
[125] Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H. Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification 2018.
[126] He K, Gkioxari G, Dollár P, Girshick R. Mask r-cnn. Proceedings of the IEEE international conference on computer vision, 2017, p. 2961–9.
[127] Zhang Y, Chan S, Park VY, Chang K-T, Mehta S, Kim MJ, et al. Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images. Acad Radiol 2022;29:S135–44. https://doi.org/10.1016/j.acra.2020.12.001.
[128] Irfan R, Almazroi AA, Rauf HT, Damaševičius R, Nasr EA, Abdelgawad AE. Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion. Diagnostics 2021;11:1212. https://doi.org/10.3390/diagnostics11071212.
[129] Di Salvo F, Doerrich S, Ledig C. MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions 2024.
