  Recently, self-supervised instance discrimination methods have achieved
significant success in learning visual representations from unlabeled
photographic images. However, given the marked differences between photographic
and medical images, the efficacy of instance-based objectives, focusing on
learning the most discriminative global features in the image (i.e., wheels in
bicycle), remains unknown in medical imaging. Our preliminary analysis showed
that high global similarity of medical images in terms of anatomy hampers
instance discrimination methods for capturing a set of distinct features,
negatively impacting their performance on medical downstream tasks. To
alleviate this limitation, we have developed a simple yet effective
self-supervised framework, called Context-Aware instance Discrimination (CAiD).
CAiD aims to improve instance discrimination learning by providing finer and
more discriminative information encoded from a diverse local context of
unlabeled medical images. We conduct a systematic analysis to investigate the
utility of the learned features from a three-pronged perspective: (i)
generalizability and transferability, (ii) separability in the embedding space,
and (iii) reusability. Our extensive experiments demonstrate that CAiD (1)
enriches representations learned from existing instance discrimination methods;
(2) delivers more discriminative features by adequately capturing finer
contextual information from individual medial images; and (3) improves
reusability of low/mid-level features compared to standard instance
discriminative methods. As open science, all codes and pre-trained models are
available on our GitHub page: https://github.com/JLiangLab/CAiD.
