SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model
This work addresses automated skin cancer diagnosis for medical applications, but it is incremental as it adapts an existing model to a specific domain.
The paper tackled skin cancer segmentation by fine-tuning the Segment Anything Model (SAM) on the HAM10000 dataset, achieving a mean pixel accuracy of 0.945 and mean dice score of 0.8879.
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.