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SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model

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arxiv 2304.13973 v1 pith:OWWDRPI7 submitted 2023-04-27 cs.CV

Mingzhe Hu , Yuheng Li , Xiaofeng Yang This is my paper · ORCID
classification cs.CV
keywords modelsegmentationmeanskinaccuracyanythingcancerfinetuned
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation

    cs.CV 2025-07 conditional novelty 4.0 of 10

    FA-SAM automates SAM-based medical segmentation across domains by generating prompt boxes with an uncertainty-enhanced network and fusing image and prompt embeddings.

  2. Mobile Image Analysis Application for Mantoux Skin Test

    eess.IV 2025-06 reject novelty 4.0 of 10

    A mobile app measures tuberculin skin test indurations with ARCore and DeepLabv3, but its accuracy claims rest on circular tests with clay mock-ups.

  3. Deep Skin Lesion Segmentation with Transformer-CNN Fusion: Toward Intelligent Skin Cancer Analysis

    eess.IV 2025-08 reject novelty 3.0 of 10

    The paper claims an improved TransUNet with boundary guidance and multi-scale fusion achieves state-of-the-art skin lesion segmentation on ISIC, but provides insufficient experimental evidence.

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