Fine-grained Multi-class Nuclei Segmentation with Molecular-empowered All-in-SAM Model

• URL: http://arxiv.org/abs/2508.15751v1
• Date: Thu, 21 Aug 2025 17:49:21 GMT
• Title: Fine-grained Multi-class Nuclei Segmentation with Molecular-empowered All-in-SAM Model
• Authors: Xueyuan Li, Can Cui, Ruining Deng, Yucheng Tang, Quan Liu, Tianyuan Yao, Shunxing Bao, Naweed Chowdhury, Haichun Yang, Yuankai Huo,
• Abstract summary: We propose the molecular-empowered All-in-SAM Model to advance computational pathology.<n>This model incorporates a full-stack approach, focusing on: (1) annotation-engaging lay annotators through molecular-empowered learning to reduce the need for detailed pixel-level annotations, and (2) learning-adapting the SAM model to emphasize specific semantics.
• Score: 15.622506758735476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Recent developments in computational pathology have been driven by advances in Vision Foundation Models, particularly the Segment Anything Model (SAM). This model facilitates nuclei segmentation through two primary methods: prompt-based zero-shot segmentation and the use of cell-specific SAM models for direct segmentation. These approaches enable effective segmentation across a range of nuclei and cells. However, general vision foundation models often face challenges with fine-grained semantic segmentation, such as identifying specific nuclei subtypes or particular cells. Approach: In this paper, we propose the molecular-empowered All-in-SAM Model to advance computational pathology by leveraging the capabilities of vision foundation models. This model incorporates a full-stack approach, focusing on: (1) annotation-engaging lay annotators through molecular-empowered learning to reduce the need for detailed pixel-level annotations, (2) learning-adapting the SAM model to emphasize specific semantics, which utilizes its strong generalizability with SAM adapter, and (3) refinement-enhancing segmentation accuracy by integrating Molecular-Oriented Corrective Learning (MOCL). Results: Experimental results from both in-house and public datasets show that the All-in-SAM model significantly improves cell classification performance, even when faced with varying annotation quality. Conclusions: Our approach not only reduces the workload for annotators but also extends the accessibility of precise biomedical image analysis to resource-limited settings, thereby advancing medical diagnostics and automating pathology image analysis.

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