Abstract
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e.g., radiological scores). Access to high-confidence labels, such as histology-based diagnoses, is rare and costly. Pretraining strategies, like contrastive learning (CL) methods, can leverage unlabeled or weakly-annotated datasets. These methods typically require large batch sizes, which poses a difficulty in the case of large 3D images at full resolution, due to limited GPU memory. Nevertheless, volumetric positional information about the spatial context of each 2D slice can be very important for some medical applications. In this work, we propose an efficient weakly-supervised positional (WSP) contrastive learning strategy where we integrate both the spatial context of each 2D slice and a weak label via a generic kernel-based loss function. We illustrate our method on cirrhosis prediction using a large volume of weakly-labeled images, namely radiological low-confidence annotations, and small strongly-labeled (i.e., high-confidence) datasets. The proposed model improves the classification AUC by 5% with respect to a baseline model on our internal dataset, and by 26% on the public LIHC dataset from the Cancer Genome Atlas. The code is available at: https://github.com/Guerbet-AI/wsp-contrastive.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Azizi, S., et al.: Big self-supervised models advance medical image classification. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3458–3468 (2021)
Barbano, C.A., Dufumier, B., Duchesnay, E., Grangetto, M., Gori, P.: Contrastive learning for regression in multi-site brain age prediction. In: IEEE ISBI (2022)
Barbano, C.A., Dufumier, B., Tartaglione, E., Grangetto, M., Gori, P.: Unbiased Supervised Contrastive Learning. In: ICLR (2023)
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 12546–12558. Curran Associates, Inc. (2020)
Chen, T., Kornblith, S., Norouzi, M., et al.: A simple framework for contrastive learning of visual representations. In: 37th International Conference on Machine Learning (ICML) (2020)
Chen, T., Kornblith, S., Swersky, K., et al.: Big self-supervised models are strong semi-supervised learners. In: NeurIPS (2020)
Chen, X., He, K.: Exploring simple Siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2020)
Dufumier, B., et al.: Contrastive learning with continuous proxy meta-data for 3D MRI classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 58–68. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_6
Erickson, B.J., Kirk, S., Lee, et al.: Radiology data from the cancer genome atlas colon adenocarcinoma [TCGA-COAD] collection. (2016)
Grill, J.B., Strub, F., Altché, F., et al.: Bootstrap your own latent - a new approach to self-supervised learning. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 21271–21284. Curran Associates, Inc. (2020)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9726–9735 (2020)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Khosla, P., Teterwak, P., Wang, C., et al.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661–18673 (2020)
Li, Q., Yu, B., Tian, X., Cui, X., Zhang, R., Guo, Q.: Deep residual nets model for staging liver fibrosis on plain CT images. Int. J. Comput. Assist. Radiol. Surg. 15(8), 1399–1406 (2020). https://doi.org/10.1007/s11548-020-02206-y
Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (2017)
Mohamadnejad, M., et al.: Histopathological study of chronic hepatitis B: a comparative study of Ishak and METAVIR scoring systems. Int. J. Organ Transp. Med. 1 (2010)
Riba, E., Mishkin, D., Ponsa, D., Rublee, E., Bradski, G.: Kornia: an open source differentiable computer vision library for PyTorch. In: Winter Conference on Applications of Computer Vision (2020)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sarfati, E., Bone, A., Rohe, M.M., Gori, P., Bloch, I.: Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies. In: IEEE ISBI. Cartagena de Indias, Colombia (Apr 2023)
Shiha, G., Zalata, K.: Ishak versus METAVIR: Terminology, convertibility and correlation with laboratory changes in chronic hepatitis C. In: Takahashi, H. (ed.) Liver Biopsy, chap. 10. IntechOpen, Rijeka (2011)
Taleb, A., Kirchler, M., Monti, R., Lippert, C.: Contig: Self-supervised multimodal contrastive learning for medical imaging with genetics. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20908–20921 (June 2022)
Wang, X., Qi, G.J.: Contrastive learning with stronger augmentations. CoRR abs/2104.07713 (2021)
Wen, J., et al.: Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Med. Image Anal. 63, 101694 (2020)
Yin, Y., Yakar, D., Dierckx, R.A.J.O., Mouridsen, K.B., Kwee, T.C., de Haas, R.J.: Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model. Eur. Radiol. 31(12), 9620–9627 (2021). https://doi.org/10.1007/s00330-021-08046-x
Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: Self-supervised learning via redundancy reduction. In: International Conference on Machine Learning (2021)
Zeng, D., et al.: Positional contrastive learning for volumetric medical image segmentation. In: MICCAI, pp. 221–230. Springer-Verlag, Berlin, Heidelberg (2021)
Zhang, P., Wang, F., Zheng, Y.: Self supervised deep representation learning for fine-grained body part recognition. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 578–582 (2017)
Zhou, Z., et al.: Models genesis: generic autodidactic models for 3D medical image analysis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 384–393. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_42
Zhuang, X., Li, Y., Hu, Y., Ma, K., Yang, Y., Zheng, Y.: Self-supervised feature learning for 3D medical images by playing a Rubik’s cube. In: MICCAI (2019)
Acknowledgments
This work was supported by Région Ile-de-France (ChoTherIA project) and ANRT (CIFRE #2021/1735).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Compliance with Ethical Standards
This research study was conducted retrospectively using human data collected from various medical centers, whose Ethics Committees granted their approval. Data was de-identified and processed according to all applicable privacy laws and the Declaration of Helsinki.
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
Sarfati, E., Bône, A., Rohé, MM., Gori, P., Bloch, I. (2023). Weakly-Supervised Positional Contrastive Learning: Application to Cirrhosis Classification. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-43907-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43906-3
Online ISBN: 978-3-031-43907-0
eBook Packages: Computer ScienceComputer Science (R0)