mlsanityData to 14 Oct 2026

SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model

arXiv 2304.13973 ↗First posted 27 Apr 2023cs.CVcited by 32

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Abstract (arXiv)

Skin cancer is a prevalent and potentially fatal disease that requires accurate and efficient diagnosis and treatment. Although manual tracing is the current standard in clinics, automated tools are desired to reduce human labor and improve accuracy. However, developing such tools is challenging due to the highly variable appearance of skin cancers and complex objects in the background. In this paper, we present SkinSAM, a fine-tuned model based on the Segment Anything Model that showed outstanding segmentation performance. The models are validated on HAM10000 dataset which includes 10015 dermatoscopic images. While larger models (ViT_L, ViT_H) performed better than the smaller one (ViT_b), the finetuned model (ViT_b_finetuned) exhibited the greatest improvement, with a Mean pixel accuracy of 0.945, Mean dice score of 0.8879, and Mean IoU score of 0.7843. Among the lesion types, vascular lesions showed the best segmentation results. Our research demonstrates the great potential of adapting SAM to medical image segmentation tasks.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 27 Apr 2023.

Builds on

8 of 17 references.

References · 8

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2023Segment Anythingcited by 8,234
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  4. 2017Mask R-CNNcited by 7,277
  5. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,446
  6. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  7. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 422
  8. 2018nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentationcited by 291

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 14; 2024: 13; 2025: 4; 2026: 1.

32 papers.

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  1. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  2. 2023Polyp-SAM: Transfer SAM for Polyp Segmentationcited by 64
  3. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  4. 2024Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decodingcited by 48
  5. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  6. 2024RobustSAM: Segment Anything Robustly on Degraded Imagescited by 36
  7. 2024How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Modelcited by 32
  8. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  9. 2023Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomycited by 27
  10. 2023On the Robustness of Segment Anythingcited by 25
  11. 2023Segment Anything Model (SAM) for Radiation Oncologycited by 24
  12. 2023Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Surveycited by 21
20 more
  1. 2023Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learningcited by 18
  2. 2024On Efficient Variants of Segment Anything Model: A Surveycited by 16
  3. 2023BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Imagescited by 7
  4. 2023TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shotcited by 7
  5. 2023Large Scale Foundation Models for Intelligent Manufacturing Applications: A Surveycited by 6
  6. 2023Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image Segmentationcited by 5
  7. 2024Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Surveycited by 5
  8. 2025Deep Skin Lesion Segmentation with Transformer-CNN Fusion: Toward Intelligent Skin Cancer Analysiscited by 5
  9. 2023Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaulcited by 4
  10. 2024Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAMcited by 4
  11. 2023Empirical Analysis of a Segmentation Foundation Model in Prostate Imagingcited by 3
  12. 2024SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervisioncited by 2
  13. 2024Foundation AI Model for Medical Image Segmentationcited by 2
  14. 2025PG-SAM: Prior-Guided SAM with Medical for Multi-organ Segmentationcited by 2
  15. 2024Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentationcited by 1
  16. 2024Urban Waterlogging Detection: A Challenging Benchmark and Large-Small Model Co-Adaptercited by 1
  17. 2024Adaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentationcited by 1
  18. 2025Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentationcited by 1
  19. 2025SAM-DCE: Addressing Token Uniformity and Semantic Over-Smoothing in Medical Segmentationcited by 0
  20. 2026PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentationcited by 0