DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis

Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can sig-nificant...

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Veröffentlicht in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Jg. 2022; S. 20792 - 20802
Hauptverfasser: Haghighi, Fatemeh, Taher, Mohammad Reza Hosseinzadeh, Gotway, Michael B., Liang, Jianming
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.06.2022
Schlagworte:
Adversarial machine learning
Annotations
biological and cell microscopy; Self-& semi-& meta- & unsupervised learning
Computer vision
Medical
Representation learning
Self-supervised learning
Semantics
Visualization
ISSN:1063-6919, 1063-6919
Online-Zugang:Volltext
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Zusammenfassung:Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can sig-nificantly benefit deep semantic representation learning. To realize this vision, we have developed DiRA, thefirstframework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-grained semantic representation learning. Our extensive experiments demonstrate that DiRA (1) encourages collaborative learning among three learning ingredients, resulting in more generalizable representation across organs, diseases, and modalities; (2) outperforms fully supervised ImageNet models and increases robustness in small data regimes, reducing annotation cost across multiple medical imaging applications; (3) learns fine-grained semantic representation, facilitating accurate lesion localization with only image-level annotation; and (4) enhances state-of-the-art restorative approaches, revealing that DiRA is a general mechanism for united representation learning. All code and pretrained models are available at https://github.com/JLiangLab/DiRA.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR52688.2022.02016