Summary of Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with Sam, by Xin Hu et al.
Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAM
by Xin Hu, Janet Wang, Jihun Hamm, Rie R Yotsu, Zhengming Ding
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel Cross-Attentive Fusion framework for interpretable skin lesion diagnosis, leveraging the Segment Anything Model (SAM) to generate visual concepts for skin diseases using prompts. The SAM model enables the automation of segmentation with simple yet effective prompts, facilitating promptable segmentation. The proposed method integrates local visual concepts with global image features to enhance model performance, demonstrating effectiveness on two skin disease datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Skin images are challenging to diagnose due to limited annotated datasets and variations in conditions. Previous methods require fine-grained ground truth masks for training, but a new approach called SAM has been introduced for promptable segmentation. However, most efforts focus on dermatoscopy images, which have clearer lesion boundaries than smartphone photos used in real-world applications. To overcome these challenges, the proposed Cross-Attentive Fusion framework uses SAM to generate visual concepts for skin diseases and combines local and global features to improve diagnosis. |
Keywords
» Artificial intelligence » Sam
