DeepSet SimCLR: Self-supervised deep sets for improved pathology representation learning

arXiv:2402.15598v1 Announce Type: new
Abstract: Often, applications of self-supervised learning to 3D medical data opt to use 3D variants of successful 2D network architectures. Although promising approaches, they are significantly more computationally demanding to train, and thus reduce the widespread applicability of these methods away from those with modest computational resources. Thus, in this paper, we aim to improve standard 2D SSL algorithms by modelling the inherent 3D nature of these datasets implicitly. We propose two variants that build upon …

abstract applications architectures arxiv computational cs.cv data deepset medical medical data network pathology reduce representation representation learning self-supervised learning supervised learning train type variants