DiRA: Discriminative, Restorative, and Adversarial Learning for
Self-supervised Medical Image Analysis
- URL: http://arxiv.org/abs/2204.10437v1
- Date: Thu, 21 Apr 2022 23:52:52 GMT
- Title: DiRA: Discriminative, Restorative, and Adversarial Learning for
Self-supervised Medical Image Analysis
- Authors: Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway,
Jianming Liang
- Abstract summary: DiRA is a framework that unites discriminative, restorative, and adversarial learning.
It gleans complementary visual information from unlabeled medical images for semantic representation learning.
- Score: 7.137224324997715
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 significantly benefit
deep semantic representation learning. To realize this vision, we have
developed DiRA, the first framework 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 pre-trained
models are available at https: //github.com/JLiangLab/DiRA.
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