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Multiple instance learning exhibits a powerful approach for whole slide image-based diagnosis in the absence of pixel- or patch-level annotations.
Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2424–2433 (2016)
2016
Earlier work this paper cites.
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: Proceedings of the International Conference on Machine Learning (ICML). pp. 2127–2136 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.M., Ciompi, F., van der Laak, J.: Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Medical Image Analysis 58
2019
Earlier work this paper cites.
Thulasidasan, S., Chennupati, G., Bilmes, J.A., Bhattacharya, T., Michalak, S.: On mixup training: Improved calibration and predictive uncertainty for deep neural networks. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 32 (2019)
2019
Earlier work this paper cites.
Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. In: Proceedings of the International Conference on Machine Learning (ICML). vol. 97, pp. 6438–6447 (2019)
2019
Earlier work this paper cites.
Buddhavarapu, V.G., Jothi, A.A.: An experimental study on classification of thyroid histopathology images using transfer learning. Pattern Recognition Letters 140
2020
Earlier work this paper cites.
Chikontwe, P., Kim, M., Nam, S.J., Go, H., Park, S.H.: Multiple instance learning with center embeddings for histopathology classification. In: Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). pp. 519–528 (2020)
2020
Cited alongside, same era.
Lerousseau, M., Vakalopoulou, M., Classe, M., Adam, J., Battistella, E., Carré, A., Estienne, T., Henry, T., Deutsch, E., Paragios, N.: Weakly supervised multiple instance learning histopathological tumor segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI) (2020)
2020
Cited alongside, same era.
Dabouei, A., Soleymani, S., Taherkhani, F., Nasrabadi, N.M.: Supermix: Supervising the mixing data augmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 13794–13803 (2021)
2021
Cited alongside, same era.
Rymarczyk, D., Borowa, A., Tabor, J., Zielinski, B.: Kernel self-attention for weakly-supervised image classification using deep multiple instance learning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 1721–1730 (2021)
2021
Later among the works it cites.
Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., zhang, y.: Transmil: Transformer based correlated multiple instance learning for whole slide image classification. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 34, pp. 2136–2147 (2021)
2021
Later among the works it cites.
Sharma, Y., Shrivastava, A., Ehsan, L., Moskaluk, C.A., Syed, S., Brown, D.: Cluster-to-conquer: A framework for end-to-end multi-instance learning for whole slide image classification. In: Proceedings of the Medical Imaging with Deep Learning Conference (MIDL). pp. 682–698 (2021)
2021
Later among the works it cites.
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Gadermayr, M., Tschuchnig, M., Stangassinger, L.M., Kreutzer, C., Couillard-Despres, S., Oostingh, G.J., Hittmair, A.: Frozen-to-paraffin: Categorization of histological frozen sections by the aid of paraffin sections and generative adversarial networks. In: Proceedings of the MICCAI Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI). pp. 99–109 (2021)
2021
Cited alongside, same era.
Galdran, A., Carneiro, G., Ballester, M.A.G.: Balanced-MixUp for highly imbalanced medical image classification. In: Proceedings of the Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). pp. 323–333 (2021)
2021
Cited alongside, same era.
Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14318–14328 (2021), https://github.com/binli123/dsmil-wsi
2021
Cited alongside, same era.
Li, Z., Zhao, W., Shi, F., Qi, L., Xie, X., Wei, Y., Ding, Z., Gao, Y., Wu, S., Liu, J., et al.: A novel multiple instance learning framework for covid-19 severity assessment via data augmentation and self-supervised learning. Medical Image Analysis 69
2021
Cited alongside, same era.
Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Huang, J., Yang, W., Han, X.: TransPath: Transformer-based self-supervised learning for histopathological image classification. In: Proceedings of the Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). pp. 186–195 (2021)
2021
Later among the works it cites.
Chen, J.N., Sun, S., He, J., Torr, P.H., Yuille, A., Bai, S.: Transmix: Attend to mix for vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 12135–12144 (2022)
2022
Closest in time.
Xi, N.M., Wang, L., Yang, C.: Improving the diagnosis of thyroid cancer by machine learning and clinical data. Scientific Reports 12
2022
Closest in time.
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 (CVPR). pp. 18802–18812 (2022)
2022
Closest in time.