Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging

<b>Background/Objectives</b>: Precise nuclei instance segmentation is a prerequisite for reliable digital pathology, yet the scarcity of pixel-level annotations remains a significant bottleneck for deep learning models. <b>Methods</b>: We propose a self-evolving framework for robust nuclei segmentat...

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Detalhes bibliográficos

Principais autores:	Siwoo Nam, Sang Hyun Park
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2026-04-01
Colecção:	Diagnostics
Assuntos:	nuclei instance segmentation Segment Anything Model (SAM) weakly supervised learning contrastive learning pseudo-labeling
Acesso em linha:	https://www.mdpi.com/2075-4418/16/9/1370
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