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Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13680))

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Abstract

Few-shot semantic segmentation is a promising solution for scarce data scenarios, especially for medical imaging challenges with limited training data. However, most of the existing few-shot segmentation methods tend to over rely on the images containing target classes, which may hinder its utilization of medical imaging data. In this paper, we present a few-shot segmentation model that employs anatomical auxiliary information from medical images without target classes for dual contrastive learning. The dual contrastive learning module performs comparison among vectors from the perspectives of prototypes and contexts, to enhance the discriminability of learned features and the data utilization. Besides, to distinguish foreground features from background features more friendly, a constrained iterative prediction module is designed to optimize the segmentation of the query image. Experiments on two medical image datasets show that the proposed method achieves performance comparable to state-of-the-art methods. Code is available at: https://github.com/cvszusparkle/AAS-DCL_FSS.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61973221 and Grant 62273241, the Natural Science Foundation of Guangdong Province of China under Grant 2018A030313381 and Grant 2019A1515011165.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Huisi Wu, Fangyan Xiao & Chongxin Liang

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  1. Huisi Wu
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  2. Fangyan Xiao
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  3. Chongxin Liang
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Corresponding author

Correspondence to Huisi Wu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Wu, H., Xiao, F., Liang, C. (2022). Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13680. Springer, Cham. https://doi.org/10.1007/978-3-031-20044-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-20044-1_24

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