Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAM Empowerment
<|reference_start|>Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAM Empowerment: Current AI-assisted skin image diagnosis has achieved dermatologist-level performance in classifying skin cancer, driven by rapid advancements in deep learning architectures. However, unlike traditional vision tasks, skin images in general present unique challenges due to the limited availability of well-annotated datasets, complex variations in conditions, and the necessity for detailed interpretations to ensure patient safety. Previous segmentation methods have sought to reduce image noise and enhance diagnostic performance, but these techniques require fine-grained, pixel-level ground truth masks for training. In contrast, with the rise of foundation models, the Segment Anything Model (SAM) has been introduced to facilitate promptable segmentation, enabling the automation of the segmentation process with simple yet effective prompts. Efforts applying SAM predominantly focus on dermatoscopy images, which present more easily identifiable lesion boundaries than clinical photos taken with smartphones. This limitation constrains the practicality of these approaches to real-world applications. To overcome the challenges posed by noisy clinical photos acquired via non-standardized protocols and to improve diagnostic accessibility, we propose a novel Cross-Attentive Fusion framework for interpretable skin lesion diagnosis. Our method leverages SAM to generate visual concepts for skin diseases using prompts, integrating local visual concepts with global image features to enhance model performance. Extensive evaluation on two skin disease datasets demonstrates our proposed method's effectiveness on lesion diagnosis and interpretability.<|reference_end|>
@article{hu2024enhancing,
    title={Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery
  with SAM Empowerment},
    author={Xin Hu, Janet Wang, Jihun Hamm, Rie R Yotsu, Zhengming Ding},
    journal={arXiv preprint arXiv:2409.09520},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09520},
    primaryClass={cs.CV cs.AI}
}