Abstract
Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data distribution, and instead they only learn a bag-level or instance-level decision boundary discriminatively by training a classifier. In this paper, we propose DGMIL: a feature distribution guided deep MIL framework for WSI classification and positive patch localization. Instead of designing complex discriminative network architectures, we reveal that the inherent feature distribution of histopathological image data can serve as a very effective guide for instance classification. We propose a cluster-conditioned feature distribution modeling method and a pseudo label-based iterative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAMELYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.
L. Qu and X. Luo—Contributed equally to this work.
Code is available at https://github.com/miccaiif/DGMIL.
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References
Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 (2017)
Bi, Q., Qin, K., Li, Z., Zhang, H., Xu, K., Xia, G.S.: A multiple-instance densely-connected convnet for aerial scene classification. IEEE Trans. Image Process. 29, 4911–4926 (2020)
Bi, Q., et al.: Local-global dual perception based deep multiple instance learning for retinal disease classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 55–64. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_6
Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301–1309 (2019)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (ICML), pp. 1597–1607. PMLR (2020)
Cheplygina, V., de Bruijne, M., Pluim, J.P.: Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med. Image Anal. 54, 280–296 (2019)
Chikontwe, P., Kim, M., Nam, S.J., Go, H., Park, S.H.: Multiple instance learning with center embeddings for histopathology classification. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 519–528. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_50
Cornish, T.C., Swapp, R.E., Kaplan, K.J.: Whole-slide imaging: routine pathologic diagnosis. Adv. Anat. Pathol. 19(3), 152–159 (2012)
Couture, H.D., Marron, J.S., Perou, C.M., Troester, M.A., Niethammer, M.: Multiple instance learning for heterogeneous images: training a CNN for histopathology. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 254–262. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_29
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377 (2021)
He, L., Long, L.R., Antani, S., Thoma, G.R.: Histology image analysis for carcinoma detection and grading. Comput. Methods Programs Biomed. 107(3), 538–556 (2012)
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning (ICML), pp. 2127–2136. PMLR (2018)
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 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14318–14328 (2021)
Li, H., Yang, F., Zhao, Yu., Xing, X., Zhang, J., Gao, M., Huang, J., Wang, L., Yao, J.: DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 206–216. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_20
Li, S., et al.: Multi-instance multi-scale CNN for medical image classification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 531–539. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_58
Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555–570 (2021)
Myronenko, A., Xu, Z., Yang, D., Roth, H.R., Xu, D.: Accounting for dependencies in deep learning based multiple instance learning for whole slide imaging. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 329–338. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_32
Pantanowitz, L., et al.: Review of the current state of whole slide imaging in pathology. J. Pathol. Inform. 2, 2–36 (2011)
Rony, J., Belharbi, S., Dolz, J., Ayed, I.B., McCaffrey, L., Granger, E.: Deep weakly-supervised learning methods for classification and localization in histology images: a survey. arXiv preprint arXiv:1909.03354 (2019)
Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al.: TransMIL: transformer based correlated multiple instance learning for whole slide image classification. In: Advances in Neural Information Processing Systems (NIPS), vol. 34 (2021)
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: Medical Imaging with Deep Learning (MIDL), pp. 682–698. PMLR (2021)
Shi, X., Xing, F., Xie, Y., Zhang, Z., Cui, L., Yang, L.: Loss-based attention for deep multiple instance learning. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 34, pp. 5742–5749 (2020)
Srinidhi, C.L., Ciga, O., Martel, A.L.: Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021)
Xu, Y., Zhu, J.Y., Chang, E., Tu, Z.: Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 964–971. IEEE (2012)
Zhao, Y., 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 (CVPR), pp. 4837–4846 (2020)
Acknowledgments
This work was supported by National Natural Science Foundation of China under Grant 82072021. The TCGA Lung Cancer dataset is from the TCGA Research Network: https://www.cancer.gov/tcga.
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Qu, L., Luo, X., Liu, S., Wang, M., Song, Z. (2022). DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_3
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