Adapting Self-Supervised Learning for Computational Pathology

arXiv:2405.01688v1 Announce Type: new
Abstract: Self-supervised learning (SSL) has emerged as a key technique for training networks that can generalize well to diverse tasks without task-specific supervision. This property makes SSL desirable for computational pathology, the study of digitized images of tissues, as there are many target applications and often limited labeled training samples. However, SSL algorithms and models have been primarily developed in the field of natural images and whether their performance can be improved by adaptation to particular …

abstract applications arxiv computational cs.cv diverse images key networks pathology property samples self-supervised learning ssl study supervised learning supervision tasks training type