DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis
This work addresses the need for more efficient and robust medical image analysis by reducing annotation costs and improving performance across various medical imaging applications, though it is incremental as it builds on existing self-supervised learning techniques.
The paper tackles the problem of self-supervised learning in medical image analysis by proposing DiRA, a framework that unites discriminative, restorative, and adversarial learning, resulting in improved representation learning that outperforms fully supervised models and enhances lesion localization with image-level annotations.
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.