mlsanityData to 7 Oct 2026

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

arXiv 2304.12620 ↗First posted 25 Apr 2023cs.CVcited by 485

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Abstract (arXiv)

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation, since the lack of the medical specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. In this paper, instead of fine-tuning the SAM model, we propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model using a light yet effective adaptation technique. In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. We conduct comprehensive evaluation experiments on 17 medical image segmentation tasks across various image modalities. Med-SA outperforms several state-of-the-art (SOTA) medical image segmentation methods, while updating only 2% of the parameters. Our code is released at https://github.com/KidsWithTokens/Medical-SAM-Adapter.

Key facts. Cited by 485 papers in the map (data through 7 Oct 2026). Builds on 40 of its 77 references that are in the map. First posted on arXiv 25 Apr 2023.

Builds on

40 of 77 references.

References · 40

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  6. 2023Segment Anythingcited by 8,158
  7. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  8. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  9. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  10. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,234
  11. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  12. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
28 more
  1. 2022AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognitioncited by 714
  2. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 703
  3. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
  4. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  5. 2021Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depthcited by 411
  6. 2019Transfusion: Understanding Transfer Learning for Medical Imagingcited by 366
  7. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 347
  8. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 279
  9. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  10. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  11. 2019REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographscited by 226
  12. 2019BCN20000: Dermoscopic Lesions in the Wildcited by 224
  13. 2021Diffusion Models for Implicit Image Segmentation Ensemblescited by 198
  14. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Modelcited by 180
  15. 2021Reviving Iterative Training with Mask Guidance for Interactive Segmentationcited by 162
  16. 2023Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imagingcited by 161
  17. 2016Deep Interactive Object Selectioncited by 160
  18. 2022FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformercited by 133
  19. 2020Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstractioncited by 70
  20. 2023Large AI Models in Health Informatics: Applications, Challenges, and the Futurecited by 61
  21. 2018Iteratively Trained Interactive Segmentationcited by 52
  22. 2019Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentationcited by 46
  23. 2023Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentationcited by 41
  24. 2021Boundary-aware Transformers for Skin Lesion Segmentationcited by 39
  25. 2019Deep Image-to-Video Adaptation and Fusion Networks for Action Recognitioncited by 21
  26. 2020Getting to 99% Accuracy in Interactive Segmentationcited by 19
  27. 2023Focused and Collaborative Feedback Integration for Interactive Image Segmentationcited by 17
  28. 2019Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challengecited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 85; 2024: 172; 2025: 153; 2026: 75.

485 papers.

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  1. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  2. 2024Medical SAM 2: Segment medical images as video via Segment Anything Model 2cited by 151
  3. 2023On the Challenges and Perspectives of Foundation Models for Medical Image Analysiscited by 135
  4. 2023SAM-Med2Dcited by 123
  5. 2023Foundational Models Defining a New Era in Vision: A Survey and Outlookcited by 109
  6. 2023SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Imagescited by 101
  7. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  8. 2025MedSAM2: Segment Anything in 3D Medical Images and Videoscited by 88
  9. 2023MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentationcited by 82
  10. 2023Review of Large Vision Models and Visual Prompt Engineeringcited by 77
  11. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  12. 2023Foundational Models in Medical Imaging: A Comprehensive Survey and Future Visioncited by 74
38 more
  1. 2024Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactivelycited by 74
  2. 2025MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflowcited by 71
  3. 2023AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encodercited by 70
  4. 20233DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationcited by 70
  5. 2024Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Modelcited by 67
  6. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  7. 2024VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imagingcited by 62
  8. 2024SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and Morecited by 58
  9. 2023Adapting Segment Anything Model for Change Detection in HR Remote Sensing Imagescited by 56
  10. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  11. 2023Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunitycited by 54
  12. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  13. 2023How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Imagescited by 53
  14. 2024Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decodingcited by 48
  15. 2023ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Imagecited by 47
  16. 2024Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruningcited by 46
  17. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  18. 2023Annotation-free Audio-Visual Segmentationcited by 44
  19. 2023Are Natural Domain Foundation Models Useful for Medical Image Classification?cited by 44
  20. 2023Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Promptingcited by 40
  21. 2023Segment Anything Model for Medical Images?cited by 39
  22. 2023One-Prompt to Segment All Medical Imagescited by 38
  23. 2023SAMAug: Point Prompt Augmentation for Segment Anything Modelcited by 38
  24. 2023AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentationcited by 37
  25. 2024RobustSAM: Segment Anything Robustly on Degraded Imagescited by 36
  26. 2023Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentationcited by 35
  27. 2024Image Segmentation in Foundation Model Era: A Surveycited by 35
  28. 2023Collaborating Foundation Models for Domain Generalized Semantic Segmentationcited by 33
  29. 2024Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Datasetcited by 33
  30. 2023Artificial General Intelligence for Medical Imaging Analysiscited by 32
  31. 2023All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuningcited by 32
  32. 2024How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Modelcited by 32
  33. 2024DarkSAM: Fooling Segment Anything Model to Segment Nothingcited by 32
  34. 2023SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptationcited by 30
  35. 2024Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentationcited by 30
  36. 2024Moving Object Segmentation: All You Need Is SAM (and Flow)cited by 30
  37. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  38. 2024SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decompositioncited by 29

50 most cited of 485 All 485 (JSON)