# SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model arXiv: https://arxiv.org/abs/2304.13973 ยท HTML: https://mlsanity.com/paper/2304.13973/ ## 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 (most cited first, 8 of 8) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017) - [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021) - [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021) - [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021) - [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018) ## Built on by (most cited first, 30 of 32) - [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024) - [Polyp-SAM: Transfer SAM for Polyp Segmentation](https://mlsanity.com/paper/2305.00293/) (2023) - [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023) - [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024) - [A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering](https://mlsanity.com/paper/2306.06211/) (2023) - [RobustSAM: Segment Anything Robustly on Degraded Images](https://mlsanity.com/paper/2406.09627/) (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) - [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023) - [On the Robustness of Segment Anything](https://mlsanity.com/paper/2305.16220/) (2023) - [Segment Anything Model (SAM) for Radiation Oncology](https://mlsanity.com/paper/2306.11730/) (2023) - [Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.03678/) (2023) - [Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning](https://mlsanity.com/paper/2308.16466/) (2023) - [On Efficient Variants of Segment Anything Model: A Survey](https://mlsanity.com/paper/2410.04960/) (2024) - [BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images](https://mlsanity.com/paper/2305.12447/) (2023) - [TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot](https://mlsanity.com/paper/2308.06444/) (2023) - [Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey](https://mlsanity.com/paper/2312.06718/) (2023) - [Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image Segmentation](https://mlsanity.com/paper/2311.13512/) (2023) - [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024) - [Deep Skin Lesion Segmentation with Transformer-CNN Fusion: Toward Intelligent Skin Cancer Analysis](https://mlsanity.com/paper/2508.14509/) (2025) - [Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul](https://mlsanity.com/paper/2307.12674/) (2023) - [Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAM](https://mlsanity.com/paper/2409.09520/) (2024) - [Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging](https://mlsanity.com/paper/2307.03266/) (2023) - [SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervision](https://mlsanity.com/paper/2407.18097/) (2024) - [Foundation AI Model for Medical Image Segmentation](https://mlsanity.com/paper/2411.02745/) (2024) - [PG-SAM: Prior-Guided SAM with Medical for Multi-organ Segmentation](https://mlsanity.com/paper/2503.18227/) (2025) - [Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2401.02076/) (2024) - [Urban Waterlogging Detection: A Challenging Benchmark and Large-Small Model Co-Adapter](https://mlsanity.com/paper/2407.08109/) (2024) - [Adaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentation](https://mlsanity.com/paper/2410.12562/) (2024) - [Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2507.17281/) (2025) ## 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.