mlsanityData to 7 Oct 2026

Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology

arXiv 2310.00504 ↗First posted 30 Sep 2023cs.CV cs.LGcited by 4

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Abstract (arXiv)

Medical imaging plays a critical role in the diagnosis and treatment planning of various medical conditions, with radiology and pathology heavily reliant on precise image segmentation. The Segment Anything Model (SAM) has emerged as a promising framework for addressing segmentation challenges across different domains. In this white paper, we delve into SAM, breaking down its fundamental components and uncovering the intricate interactions between them. We also explore the fine-tuning of SAM and assess its profound impact on the accuracy and reliability of segmentation results, focusing on applications in radiology (specifically, brain tumor segmentation) and pathology (specifically, breast cancer segmentation). Through a series of carefully designed experiments, we analyze SAM's potential application in the field of medical imaging. We aim to bridge the gap between advanced segmentation techniques and the demanding requirements of healthcare, shedding light on SAM's transformative capabilities.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 30 Sep 2023.

Builds on

15 of 24 references.

References · 15

  1. 2023Segment Anythingcited by 8,158
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  3. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  4. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  5. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
  6. 2023Customized Segment Anything Model for Medical Image Segmentationcited by 277
  7. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 81
  8. 2023AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encodercited by 70
  9. 20233DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationcited by 70
  10. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
  11. 2023How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Imagescited by 53
  12. 2023All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuningcited by 32
3 more
  1. 2023SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathologycited by 26
  2. 2023Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelinescited by 15
  3. 2023Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imagingcited by 3

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  2. 2023A Systematic Review of Deep Learning-based Research on Radiology Report Generationcited by 14
  3. 2025Foundation Models in Medical Imaging: A Review and Outlookcited by 7
  4. 2025OctreeNCA: Single-Pass 184 MP Segmentation on Consumer Hardwarecited by 4