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In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of discriminative instances, which are closely linked to overfitting.
Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial intelligence 89
1997
Earlier work this paper cites.
Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. Advances in neural information processing systems 10
1997
Earlier work this paper cites.
Rosenberg, A., Hirschberg, J.: V-measure: A conditional entropy-based external cluster evaluation measure. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL). pp. 410–420 (2007)
2007
Earlier work this paper cites.
Madabhushi, A.: Digital pathology image analysis: opportunities and challenges. Imaging in medicine 1
2009
Earlier work this paper cites.
Pantanowitz, L., Valenstein, P.N., Evans, A.J., Kaplan, K.J., Pfeifer, J.D., Wilbur, D.C., Collins, L.C., Colgan, T.J.: Review of the current state of whole slide imaging in pathology. Journal of pathology informatics 2
2011
Earlier work this paper cites.
Cornish, T.C., Swapp, R.E., Kaplan, K.J.: Whole-slide imaging: routine pathologic diagnosis. Advances in anatomic pathology 19
2012
Earlier work this paper cites.
He, L., Long, L.R., Antani, S., Thoma, G.R.: Histology image analysis for carcinoma detection and grading. Computer methods and programs in biomedicine 107
2012
Earlier work this paper cites.
Amores, J.: Multiple instance classification: Review, taxonomy and comparative study. Artificial intelligence 201
2013
Earlier work this paper cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15
2014
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Litjens, G., Sánchez, C.I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen-Van De Kaa, C., Bult, P., Van Ginneken, B., Van Der Laak, J.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Scientific reports 6
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Bejnordi, B.E., Veta, M., Van Diest, P.J., Van Ginneken, B., Karssemeijer, N., Litjens, G., Van Der Laak, J.A., Hermsen, M., Manson, Q.F., Balkenhol, M., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama 318
2017
Earlier work this paper cites.
Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: International conference on machine learning. pp. 933–941. PMLR (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Zhu, X., Yao, J., Zhu, F., Huang, J.: Wsisa: Making survival prediction from whole slide histopathological images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7234–7242 (2017)
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International conference on machine learning. pp. 2127–2136. PMLR (2018)
2018
Earlier work this paper cites.
Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph cnn for survival analysis on whole slide pathological images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 174–182. Springer (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
Campanella, G., Hanna, M.G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K.J., Brogi, E., Reuter, V.E., Klimstra, D.S., Fuchs, T.J.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature medicine 25
2019
Cited alongside, same era.
Tellez, D., Litjens, G., van der Laak, J., Ciompi, F.: Neural image compression for gigapixel histopathology image analysis. IEEE transactions on pattern analysis and machine intelligence 43
2019
Cited alongside, same era.
Chikontwe, P., Kim, M., Nam, S.J., Go, H., Park, S.H.: Multiple instance learning with center embeddings for histopathology classification. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V 23. pp. 519–528. Springer (2020)
Guan, Y., Zhang, J., Tian, K., Yang, S., Dong, P., Xiang, J., Yang, W., Huang, J., Zhang, Y., Han, X.: Node-aligned graph convolutional network for whole-slide image representation and classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18813–18823 (2022)
2022
Later among the works it cites.
Hou, W., Yu, L., Lin, C., Huang, H., Yu, R., Qin, J., Wang, L.: Hˆ 2-mil: Exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis. In: AAAI. vol. 36, pp. 933–941 (2022)
2022
Later among the works it cites.
Kong, F., Henao, R.: Efficient classification of very large images with tiny objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2384–2394 (2022)
2022
Later among the works it cites.
Qu, L., Wang, M., Song, Z., et al.: Bi-directional weakly supervised knowledge distillation for whole slide image classification. Neurips 35
2022
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
Geirhos, R., Jacobsen, J.H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., Wichmann, F.A.: Shortcut learning in deep neural networks. Nature Machine Intelligence 2
2020
Cited alongside, same era.
Huang, Z., Wang, H., Xing, E.P., Huang, D.: Self-challenging improves cross-domain generalization. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16. pp. 124–140. Springer (2020)
2020
Cited alongside, same era.
Pinckaers, H., Van Ginneken, B., Litjens, G.: Streaming convolutional neural networks for end-to-end learning with multi-megapixel images. IEEE transactions on pattern analysis and machine intelligence 44
2020
Cited alongside, same era.
Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., Huang, J.: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Medical Image Analysis 65
2020
Cited alongside, same era.
Zhao, Y., Yang, F., Fang, Y., Liu, H., Zhou, N., Zhang, J., Sun, J., Yang, S., Menze, B., Fan, X., et al.: Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4837–4846 (2020)
2020
Cited alongside, same era.
Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 13001–13008 (2020)
2020
Cited alongside, same era.
Bejani, M.M., Ghatee, M.: A systematic review on overfitting control in shallow and deep neural networks. Artificial Intelligence Review pp. 1–48 (2021)
2021
Cited alongside, same era.
Later among the works it cites.
Wang, X., Xiang, J., Zhang, J., Yang, S., Yang, Z., Wang, M.H., Zhang, J., Yang, W., Huang, J., Han, X.: Scl-wc: Cross-slide contrastive learning for weakly-supervised whole-slide image classification. Advances in neural information processing systems 35
2022
Later among the works it cites.
Yang, J., Chen, H., Zhao, Y., Yang, F., Zhang, Y., He, L., Yao, J.: Remix: A general and efficient framework for multiple instance learning based whole slide image classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 35–45. Springer (2022)
2022
Later among the works it cites.
Yufei, C., Liu, Z., Liu, X., Liu, X., Wang, C., Kuo, T.W., Xue, C.J., Chan, A.B.: Bayes-mil: A new probabilistic perspective on attention-based multiple instance learning for whole slide images. In: The Eleventh International Conference on Learning Representations (2022)
2022
Later among the works it cites.
Zhang, H., Meng, Y., Zhao, Y., Qiao, Y., Yang, X., Coupland, S.E., Zheng, Y.: Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18802–18812 (2022)
2022
Later among the works it cites.
Zhang, Y., Sun, Y., Li, H., Zheng, S., Zhu, C., Yang, L.: Benchmarking the robustness of deep neural networks to common corruptions in digital pathology. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 242–252. Springer (2022)
2022
Later among the works it cites.
Bontempo, G., Bolelli, F., Porrello, A., Calderara, S., Ficarra, E.: A graph-based multi-scale approach with knowledge distillation for wsi classification. TMI (2023)
2023
Closest in time.
Chan, T.H., Cendra, F.J., Ma, L., Yin, G., Yu, L.: Histopathology whole slide image analysis with heterogeneous graph representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15661–15670 (2023)
2023
Closest in time.
Chen, Y.C., Lu, C.S.: Rankmix: Data augmentation for weakly supervised learning of classifying whole slide images with diverse sizes and imbalanced categories. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23936–23945 (2023)
2023
Closest in time.
2023
Closest in time.
Kang, M., Song, H., Park, S., Yoo, D., Pereira, S.: Benchmarking self-supervised learning on diverse pathology datasets. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3344–3354 (2023)
2023
Closest in time.
Li, H., Zhu, C., Zhang, Y., Sun, Y., Shui, Z., Kuang, W., Zheng, S., Yang, L.: Task-specific fine-tuning via variational information bottleneck for weakly-supervised pathology whole slide image classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7454–7463 (2023)
2023
Closest in time.
Lin, T., Yu, Z., Hu, H., Xu, Y., Chen, C.W.: Interventional bag multi-instance learning on whole-slide pathological images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19830–19839 (2023)
2023
Closest in time.
Qu, L., Yang, Z., Duan, M., Ma, Y., Wang, S., Wang, M., Song, Z.: Boosting whole slide image classification from the perspectives of distribution, correlation and magnification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 21463–21473 (2023)
2023
Closest in time.
2023
Closest in time.
Tiwari, R., Shenoy, P.: Overcoming simplicity bias in deep networks using a feature sieve (2023)
2023
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2023
Closest in time.