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.

翻译：为了实现这一愿景,我们开发了DIRA,这是将歧视、恢复性和对抗性学习统一起来的第一个框架,以统一的方式将歧视、恢复性和对抗性学习结合起来,从未贴标签的医疗图像中收集补充性视觉信息,用于精密的语义教学学习。我们的广泛实验表明,DIRA(1)鼓励三种学习成分之间的合作学习,从而在器官、疾病和模式之间形成更加普遍的代表性;(2) 超越充分监督的图像网络模型,提高小型数据系统的稳健性,降低多种医学成像应用的记号成本;(3) 学习精细的语义教学,促进精确的损害定位,只有图像级别的注解;(4) 加强艺术状态的恢复方法,揭示DIRA是统一代表性学习的一般机制。