Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications. (arXiv:2112.05760v2 [eess.IV] UPDATED)

Unsupervised learning has made substantial progress over the last few years,
especially by means of contrastive self-supervised learning. The dominating
dataset for benchmarking self-supervised learning has been ImageNet, for which
recent methods are approaching the performance achieved by fully supervised
training. The ImageNet dataset is however largely object-centric, and it is not
clear yet what potential those methods have on widely different datasets and
tasks that are not object-centric, such as in digital pathology. While
self-supervised learning has started to …

applications arxiv learning self-supervised learning supervised learning