  Learning high-quality, self-supervised, visual representations is essential
to advance the role of computer vision in biomedical microscopy and clinical
medicine. Previous work has focused on self-supervised representation learning
(SSL) methods developed for instance discrimination and applied them directly
to image patches, or fields-of-view, sampled from gigapixel whole-slide images
(WSIs) used for cancer diagnosis. However, this strategy is limited because it
(1) assumes patches from the same patient are independent, (2) neglects the
patient-slide-patch hierarchy of clinical biomedical microscopy, and (3)
requires strong data augmentations that can degrade downstream performance.
Importantly, sampled patches from WSIs of a patient's tumor are a diverse set
of image examples that capture the same underlying cancer diagnosis. This
motivated HiDisc, a data-driven method that leverages the inherent
patient-slide-patch hierarchy of clinical biomedical microscopy to define a
hierarchical discriminative learning task that implicitly learns features of
the underlying diagnosis. HiDisc uses a self-supervised contrastive learning
framework in which positive patch pairs are defined based on a common ancestry
in the data hierarchy, and a unified patch, slide, and patient discriminative
learning objective is used for visual SSL. We benchmark HiDisc visual
representations on two vision tasks using two biomedical microscopy datasets,
and demonstrate that (1) HiDisc pretraining outperforms current
state-of-the-art self-supervised pretraining methods for cancer diagnosis and
genetic mutation prediction, and (2) HiDisc learns high-quality visual
representations using natural patch diversity without strong data
augmentations.
