Abstract
Digital pathology plays a pivotal role in the diagnosis and interpretation of diseases and has drawn increasing attention in modern healthcare. Due to the huge gigapixel-level size and diverse nature of whole-slide images (WSIs), analyzing them through multiple instance learning (MIL) has become a widely-used scheme, which, however, faces the challenges that come with the weakly supervised nature of MIL. Conventional MIL methods mostly either utilized instance-level or bag-level supervision to learn informative representations from WSIs for downstream tasks. In this work, we propose a novel MIL method for pathological image analysis with integrated instance-level and bag-level supervision (termed IIB-MIL). More importantly, to overcome the weakly supervised nature of MIL, we design a label-disambiguation-based instance-level supervision for MIL using Prototypes and Confidence Bank to reduce the impact of noisy labels. Extensive experiments demonstrate that IIB-MIL outperforms state-of-the-art approaches in both benchmarking datasets and addressing the challenging practical clinical task. The code is available at https://github.com/TencentAILabHealthcare/IIB-MIL.
Q. Ren and Y. Zhao—Equally-contributed authors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amores, J.: Multiple instance classification: review, taxonomy and comparative study. Artif. intell. 201, 81–105 (2013)
Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301–1309 (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
Coudray, N., et al.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559–1567 (2018)
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
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 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2424–2433 (2016)
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127–2136. PMLR (2018)
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: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 470–479. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_45
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, pp. 14318–14328 (2021)
Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph CNN for survival analysis on whole slide pathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 174–182. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_20
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Lu, M.Y., et al.: Ai-based pathology predicts origins for cancers of unknown primary. Nature 594(7861), 106–110 (2021)
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)
Noorbakhsh, J., et al.: Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat. Commun. 11(1), 6367 (2020)
Rubin, R., Strayer, D.S., Rubin, E., et al.: Rubin’s Pathology: Clinicopathologic Foundations of Medicine. Lippincott Williams & Wilkins (2008)
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. Adv. Neural Inf. Process. Syst. 34, 2136–2147 (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, pp. 682–698. PMLR (2021)
Skrede, O.J., et al.: Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. The Lancet 395(10221), 350–360 (2020)
Srinidhi, C.L., Ciga, O., Martel, A.L.: Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021)
Tellez, D., Litjens, G., van der Laak, J., Ciompi, F.: Neural image compression for gigapixel histopathology image analysis. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 567–578 (2019)
Wang, H., et al.: Pico: contrastive label disambiguation for partial label learning. arXiv preprint arXiv:2201.08984 (2022)
Wang, X., Yan, Y., Tang, P., Bai, X., Liu, W.: Revisiting multiple instance neural networks. Pattern Recogn. 74, 15–24 (2018)
Wetstein, S.C., et al.: Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images. Sci. Rep. 12(1), 15102 (2022)
Xu, G., et al.: Camel: a weakly supervised learning framework for histopathology image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10682–10691 (2019)
Zhang, H., Meng, Y., Qian, X., Yang, X., Coupland, S.E., Zheng, Y.: A regularization term for slide correlation reduction in whole slide image analysis with deep learning. In: Medical Imaging with Deep Learning, pp. 842–854. PMLR (2021)
Zhang, H., et al.: 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)
Zhao, Y., Lin, Z., Sun, K., Zhang, Y., Huang, J., Wang, L., Yao, J.: Setmil: spatial encoding transformer-based multiple instance learning for pathological image analysis. In: Medical Image Computing and Computer Assisted Intervention-MICCAI 2022: 25th International Conference, Singapore, 18–22 September 2022, Proceedings, Part II, pp. 66–76. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-16434-7_7
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, pp. 4837–4846 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ren, Q. et al. (2023). IIB-MIL: Integrated Instance-Level and Bag-Level Multiple Instances Learning with Label Disambiguation for Pathological Image Analysis. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_54
Download citation
DOI: https://doi.org/10.1007/978-3-031-43987-2_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43986-5
Online ISBN: 978-3-031-43987-2
eBook Packages: Computer ScienceComputer Science (R0)