Abstract
Previous efforts to learn histology features that correlate with specific genetic/molecular traits resort to tile-level multi-instance learning (MIL) which relies on a fixed pretrained model for feature extraction and an instance-bag classifier. We argue that such a two-step approach is not optimal at capturing both fine-grained features at tile level and global features at slide level optimal to the task. We propose a self-interactive MIL that iteratively feedbacks training information between the fine-grained and global context features. We validate the proposed approach on 4 subtyping tasks: EMT status (ovarian), KRAS mutation (colon and lung), EGFR mutation (colon), and HER2 status (breast). Our approach yields an average improvement of \(7.05\%-8.34\%\) (in terms of AUC) over the baseline.
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References
Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301–1309 (2019)
Ciga, O., Xu, T., Martel, A.L.: Self supervised contrastive learning for digital histopathology. Mach. Learn. Appl. 7, 100198 (2022)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096–2300 (2016)
Hashimoto, N., et al.: Multi-scale domain-adversarial multiple-instance CNN for cancer subtype classification with unannotated histopathological images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3852–3861 (2020)
Hu, Z., et al.: The repertoire of serous ovarian cancer non-genetic heterogeneity revealed by single-cell sequencing of normal fallopian tube epithelial cells. Cancer Cell 37(2), 226–242 (2020)
Hu, Z., et al.: The oxford classic links epithelial-to-mesenchymal transition to immunosuppression in poor prognosis ovarian cancers. Clin. Cancer Res. 27(5), 1570–1579 (2021)
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127–2136. PMLR (2018)
Kalra, S., et al.: Pay attention with focus: a novel learning scheme for classification of whole slide images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 350–359. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_34
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, H., et al.: DT-MIL: deformable transformer for multi-instance learning on histopathological image. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 206–216. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_20
Li, J., et al.: A multi-resolution model for histopathology image classification and localization with multiple instance learning. Comput. Biol. Med. 131, 104253 (2021)
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)
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Shao, Z., et al.: Transmil: transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural Inf. Process. Syst. 34 (2021)
Sirinukunwattana, K., et al.: Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning. Gut 70(3), 544–554 (2021)
Tomczak, K., Czerwińska, P., Wiznerowicz, M.: The cancer genome atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19(1A), A68 (2015)
Acknowledgements
We thank Stefano Malacrino and Nasullah Khalid Alham for providing technical support. Financial support: YU, CV and JR - National Institute for Health Research (NIHR) Oxford Biomedical Research Centre; KS, CV, and JR - Innovate UK funded PathLAKE consortium; KG - Clinical Lectureship from the National Institute for Health Research (NIHR, grant no. CL-2017-13-001); RW - EPSRC Centre for Doctoral Training in Health Data Science (EP/S02428X/1) and Oxford CRUK Centre for Cancer Research. Computation used the Oxford Biomedical Research Computing (BMRC) facility.
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Hu, Y., Sirinukunwattana, K., Gaitskell, K., Wood, R., Verrill, C., Rittscher, J. (2022). Predicting Molecular Traits from Tissue Morphology Through Self-interactive Multi-instance Learning. 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_13
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