Subject Areas : journal of Artificial Intelligence in Electrical Engineering
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Keywords:
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References:
[1] M. K. K. Niazi, A. V. Parwani, and M. N. Gurcan, “Digital pathology and artificial intelligence,” The Lancet Oncology, vol. 20, no. 5, pp. e253–e261, May 2019.
[1] M. K. K. Niazi, A. V. Parwani, and M. N. Gurcan, “Digital pathology and artificial intelligence,” The Lancet Oncology, vol. 20, no. 5, pp. e253–e261, May 2019.
[2] S. W. Jahn, M. Plass, and F. Moinfar, “Digital pathology: advantages, limitations and emerging perspectives,” Journal of Clinical Medicine, vol. 9, no. 11, p. 3697, 2020.
[3] M. G. Hanna and M. H. Hanna, “Current applications and challenges of artificial intelligence in pathology,” Human Pathology Reports, vol. 27, p. 300596, 2022.
[4] M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “Histopathological image analysis: A review,” IEEE Reviews in Biomedical Engineering, vol. 2, pp. 147–171, 2009.
[5] E. A. Rakha, M. Toss, S. Shiino, P. Gamble, R. Jaroensri, C. H. Mermel, and P. H. C. Chen, “Current and future applications of artificial intelligence in pathology: a clinical perspective,” Journal of Clinical Pathology, vol. 74, no. 7, pp. 409–414, 2021.
[6] A. Madabhushi and G. Lee, “Image analysis and machine learning in digital pathology: Challenges and opportunities,” Medical Image Analysis, vol. 33, pp. 170–175, 2016.
[7] S. B. Mukadam and H. Y. Patil, “Machine learning and computer vision based methods for cancer classification: A systematic review,” Archives of Computational Methods in Engineering, vol. 31, no. 5, pp. 3015–3050, 2024.
[8] A. Latif, A. Rasheed, U. Sajid, J. Ahmed, N. Ali, N. I. Ratyal, B. Zafar, S. H. Dar, M. Sajid, and T. Khalil, “Content‐based image retrieval and feature extraction: A comprehensive review,” Mathematical Problems in Engineering, vol. 2019, no. 1, p. 9658350, 2019.
[9] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognition, vol. 42, no. 3, pp. 425–436, 2009.
[10] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, pp. 91–110, 2004.
[11] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008.
[12] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition.
[13] T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.
[14] Ş. Öztürk and A. Bayram, “Comparison of HOG, MSER, SIFT, FAST, LBP and CANNY features for cell detection in histopathological images,” Helix, vol. 8, no. 3, pp. 3321–3325, 2018.
[15] T. Ojala, M. Pietikäinen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
[16] M. Babaie, S. Kalra, A. Sriram, C. Mitcheltree, S. Zhu, S. Khatami, A. Rahnamayan, and H. Tizhoosh, “Classification and retrieval of digital pathology scans: A new dataset,” in Proc. Workshop on Computer Vision for Microscopy Image Analysis (CVMI), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 8–16, 2017.
[17] A. Jurio, H. Bustince, M. Pagola, P. Couto, and W. Pedrycz, “New measures of homogeneity for image processing: An application to fingerprint segmentation,” Soft Computing, vol. 18, no. 6, pp. 1055–1066, 2014.
[18] F. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, “A dataset for breast cancer histopathological image classification,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455–1462, 2015.
[19] X. He, D. Cai, and P. Niyogi, “Laplacian score for feature selection,” in Proc. Int. Conf. on Neural Information Processing Systems (NIPS), pp. 507–514, 2005.
[20] D. Cai, C. Zhang, and X. He, “Unsupervised feature selection for multi-cluster data,” in Proc. ACM Int. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 333–342, 2010.
[21] Y. Yang, H. T. Shen, Z. Ma, Z. Huang, and X. Zhou, “Norm regularized discriminative feature selection for unsupervised learning,” in Proc. 22nd Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 1589–1594, 2011.
[22] M. Hall, “Correlation-based feature selection for machine learning,” Ph.D. dissertation, Dept. of Computer Science, Univ. of Waikato, Hamilton, New Zealand, 1999.
[23] H. R. Tizhoosh and M. Babaie, “Representing medical images with encoded local projections,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 10, pp. 2267–2277, 2018.
[24] B. Kieffer, M. Babaie, S. Kalra, and H. R. Tizhoosh, “Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks,” in Proc. 2017 Seventh Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6, Nov. 2017.
[25] F. A. Spanhol, L. S. Oliveira, P. R. Cavalin, C. Petitjean, and L. Heutte, “Deep features for breast cancer histopathological image classification,” in Proc. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1868–1873, Oct. 2017.
[26] F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, “Breast cancer histopathological image classification using convolutional neural networks,” in Proc. 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567, July 2016.
[1] M. K. K. Niazi, A. V. Parwani, and M. N. Gurcan, “Digital pathology and artificial intelligence,” The Lancet Oncology, vol. 20, no. 5, pp. e253–e261, May 2019.
[1] M. K. K. Niazi, A. V. Parwani, and M. N. Gurcan, “Digital pathology and artificial intelligence,” The Lancet Oncology, vol. 20, no. 5, pp. e253–e261, May 2019.
[2] S. W. Jahn, M. Plass, and F. Moinfar, “Digital pathology: advantages, limitations and emerging perspectives,” Journal of Clinical Medicine, vol. 9, no. 11, p. 3697, 2020.
[3] M. G. Hanna and M. H. Hanna, “Current applications and challenges of artificial intelligence in pathology,” Human Pathology Reports, vol. 27, p. 300596, 2022.
[4] M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “Histopathological image analysis: A review,” IEEE Reviews in Biomedical Engineering, vol. 2, pp. 147–171, 2009.
[5] E. A. Rakha, M. Toss, S. Shiino, P. Gamble, R. Jaroensri, C. H. Mermel, and P. H. C. Chen, “Current and future applications of artificial intelligence in pathology: a clinical perspective,” Journal of Clinical Pathology, vol. 74, no. 7, pp. 409–414, 2021.
[6] A. Madabhushi and G. Lee, “Image analysis and machine learning in digital pathology: Challenges and opportunities,” Medical Image Analysis, vol. 33, pp. 170–175, 2016.
[7] S. B. Mukadam and H. Y. Patil, “Machine learning and computer vision based methods for cancer classification: A systematic review,” Archives of Computational Methods in Engineering, vol. 31, no. 5, pp. 3015–3050, 2024.
[8] A. Latif, A. Rasheed, U. Sajid, J. Ahmed, N. Ali, N. I. Ratyal, B. Zafar, S. H. Dar, M. Sajid, and T. Khalil, “Content‐based image retrieval and feature extraction: A comprehensive review,” Mathematical Problems in Engineering, vol. 2019, no. 1, p. 9658350, 2019.
[9] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognition, vol. 42, no. 3, pp. 425–436, 2009.
[10] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, pp. 91–110, 2004.
[11] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008.
[12] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition.
[13] T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.
[14] Ş. Öztürk and A. Bayram, “Comparison of HOG, MSER, SIFT, FAST, LBP and CANNY features for cell detection in histopathological images,” Helix, vol. 8, no. 3, pp. 3321–3325, 2018.
[15] T. Ojala, M. Pietikäinen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
[16] M. Babaie, S. Kalra, A. Sriram, C. Mitcheltree, S. Zhu, S. Khatami, A. Rahnamayan, and H. Tizhoosh, “Classification and retrieval of digital pathology scans: A new dataset,” in Proc. Workshop on Computer Vision for Microscopy Image Analysis (CVMI), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 8–16, 2017.
[17] A. Jurio, H. Bustince, M. Pagola, P. Couto, and W. Pedrycz, “New measures of homogeneity for image processing: An application to fingerprint segmentation,” Soft Computing, vol. 18, no. 6, pp. 1055–1066, 2014.
[18] F. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, “A dataset for breast cancer histopathological image classification,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455–1462, 2015.
[19] X. He, D. Cai, and P. Niyogi, “Laplacian score for feature selection,” in Proc. Int. Conf. on Neural Information Processing Systems (NIPS), pp. 507–514, 2005.
[20] D. Cai, C. Zhang, and X. He, “Unsupervised feature selection for multi-cluster data,” in Proc. ACM Int. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 333–342, 2010.
[21] Y. Yang, H. T. Shen, Z. Ma, Z. Huang, and X. Zhou, “Norm regularized discriminative feature selection for unsupervised learning,” in Proc. 22nd Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 1589–1594, 2011.
[22] M. Hall, “Correlation-based feature selection for machine learning,” Ph.D. dissertation, Dept. of Computer Science, Univ. of Waikato, Hamilton, New Zealand, 1999.
[23] H. R. Tizhoosh and M. Babaie, “Representing medical images with encoded local projections,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 10, pp. 2267–2277, 2018.
[24] B. Kieffer, M. Babaie, S. Kalra, and H. R. Tizhoosh, “Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks,” in Proc. 2017 Seventh Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6, Nov. 2017.
[25] F. A. Spanhol, L. S. Oliveira, P. R. Cavalin, C. Petitjean, and L. Heutte, “Deep features for breast cancer histopathological image classification,” in Proc. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1868–1873, Oct. 2017.
[26] F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, “Breast cancer histopathological image classification using convolutional neural networks,” in Proc. 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567, July 2016.
