Abstract
Self-supervised contrastive representation learning offers the advantage of learning meaningful visual representations from unlabeled medical datasets for transfer learning. However, applying current contrastive learning approaches to medical data without considering its domain-specific anatomical characteristics may lead to visual representations that are inconsistent in appearance and semantics. In this paper, we propose to improve visual representations of medical images via anatomy-aware contrastive learning (AWCL), which incorporates anatomy information to augment the positive/negative pair sampling in a contrastive learning manner. The proposed approach is demonstrated for automated fetal ultrasound imaging tasks, enabling the positive pairs from the same or different ultrasound scans that are anatomically similar to be pulled together and thus improving the representation learning. We empirically investigate the effect of inclusion of anatomy information with coarse- and fine-grained granularity, for contrastive learning and find that learning with fine-grained anatomy information which preserves intra-class difference is more effective than its counterpart. We also analyze the impact of anatomy ratio on our AWCL framework and find that using more distinct but anatomically similar samples to compose positive pairs results in better quality representations. Experiments on a large-scale fetal ultrasound dataset demonstrate that our approach is effective for learning representations that transfer well to three clinical downstream tasks, and achieves superior performance compared to ImageNet supervised and the current state-of-the-art contrastive learning methods. In particular, AWCL outperforms ImageNet supervised method by 13.8% and state-of-the-art contrastive-based method by 7.1% on a cross-domain segmentation task.
Z. Fu, J. Jiao and R. Yasrab—Equal contribution.
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Notes
- 1.
Every 8th frame is extracted to reduce temporal redundancy of ultrasound videos.
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Acknowledgement
The authors would like to thank Lok Hin Lee, Richard Droste, Yuan Gao and Harshita Sharma for their help with data preparation. This work is supported by the EPSRC Programme Grants Visual AI (EP/T028572/1) and Seebibyte (EP/M013774/1), the ERC Project PULSE (ERC-ADG-2015 694581), the NIH grant U01AA014809, and the NIHR Oxford Biomedical Research Centre. The NVIDIA Corporation is thanked for a GPU donation.
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Fu, Z., Jiao, J., Yasrab, R., Drukker, L., Papageorghiou, A.T., Noble, J.A. (2023). Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13803. Springer, Cham. https://doi.org/10.1007/978-3-031-25066-8_23
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