# SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology

arXiv: https://arxiv.org/abs/2307.09570 · HTML: https://mlsanity.com/paper/2307.09570/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)
- [MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images](https://mlsanity.com/paper/1806.01963/) (2018)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning](https://mlsanity.com/paper/2303.12214/) (2023)
- [Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology](https://mlsanity.com/paper/2212.12105/) (2022)

## Built on by (most cited first, 26 of 26)

- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024)
- [How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model](https://mlsanity.com/paper/2404.09957/) (2024)
- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation](https://mlsanity.com/paper/2503.12068/) (2025)
- [ASPS: Augmented Segment Anything Model for Polyp Segmentation](https://mlsanity.com/paper/2407.00718/) (2024)
- [Segment Anything for Histopathology](https://mlsanity.com/paper/2502.00408/) (2025)
- [Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection](https://mlsanity.com/paper/2408.05892/) (2024)
- [Recent Advances in Medical Imaging Segmentation: A Survey](https://mlsanity.com/paper/2505.09274/) (2025)
- [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025)
- [Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model](https://mlsanity.com/paper/2409.09484/) (2024)
- [Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology](https://mlsanity.com/paper/2310.00504/) (2023)
- [Path-SAM2: Transfer SAM2 for digital pathology semantic segmentation](https://mlsanity.com/paper/2408.03651/) (2024)
- [PanoSAMic: Panoramic Image Segmentation from SAM Feature Encoding and Dual View Fusion](https://mlsanity.com/paper/2601.07447/) (2026)
- [PathSegDiff: Pathology Segmentation using Diffusion model representations](https://mlsanity.com/paper/2504.06950/) (2025)
- [AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology](https://mlsanity.com/paper/2602.03998/) (2026)
- [Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation](https://mlsanity.com/paper/2403.10780/) (2024)
- [VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology](https://mlsanity.com/paper/2601.16451/) (2026)
- [Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation](https://mlsanity.com/paper/2406.18249/) (2024)
- [Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models](https://mlsanity.com/paper/2503.06816/) (2025)
- [Hybrid Deep Learning-Based for Enhanced Occlusion Segmentation in PICU Patient Monitoring](https://mlsanity.com/paper/2407.13341/) (2024)
- [A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention](https://mlsanity.com/paper/2407.18673/) (2024)
- [CellPilot: A unified approach to automatic and interactive segmentation in histopathology](https://mlsanity.com/paper/2411.15514/) (2024)
- [Pathology Image Compression with Pre-trained Autoencoders](https://mlsanity.com/paper/2503.11591/) (2025)
- [SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images](https://mlsanity.com/paper/2511.08626/) (2025)
- [Learning Class Difficulty via Dynamic Focal Attention for Histopathology Segmentation](https://mlsanity.com/paper/2604.13479/) (2026)

## Abstract (arXiv)

Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have been recently proposed for universal use in segmentation tasks. SAM shows remarkable promise in instance segmentation on natural images. However, the applicability of SAM to computational pathology tasks is limited due to the following factors: (1) lack of comprehensive pathology datasets used in SAM training and (2) the design of SAM is not inherently optimized for semantic segmentation tasks. In this work, we adapt SAM for semantic segmentation by introducing trainable class prompts, followed by further enhancements through the incorporation of a pathology encoder, specifically a pathology foundation model. Our framework, SAM-Path enhances SAM's ability to conduct semantic segmentation in digital pathology without human input prompts. Through experiments on two public pathology datasets, the BCSS and the CRAG datasets, we demonstrate that the fine-tuning with trainable class prompts outperforms vanilla SAM with manual prompts and post-processing by 27.52% in Dice score and 71.63% in IOU. On these two datasets, the proposed additional pathology foundation model further achieves a relative improvement of 5.07% to 5.12% in Dice score and 4.50% to 8.48% in IOU.