Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation

arXiv:2402.16280v1 Announce Type: new
Abstract: Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning paradigms have recently attracted much research interest, such as weakly-/semi-supervised learning, generative adversarial learning, etc. In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL). Our work was motivated by that, with the prosperity of computational pathology, an increasing number …

abstract adversarial adversarial learning annotation arxiv cs.cv deep learning etc expert few-shot few-shot learning generative images instance instances paper research segmentation semi-supervised semi-supervised learning solution supervised learning type