VLM Models and Automated Grading of Atopic Dermatitis
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918032075390976 |
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| author | Lalonde, Marc Ghodrati, Hamed |
| author_facet | Lalonde, Marc Ghodrati, Hamed |
| contents | The task of grading atopic dermatitis (or AD, a form of eczema) from patient images is difficult even for trained dermatologists. Research on automating this task has progressed in recent years with the development of deep learning solutions; however, the rapid evolution of multimodal models and more specifically vision-language models (VLMs) opens the door to new possibilities in terms of explainable assessment of medical images, including dermatology. This report describes experiments carried out to evaluate the ability of seven VLMs to assess the severity of AD on a set of test images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VLM Models and Automated Grading of Atopic Dermatitis Lalonde, Marc Ghodrati, Hamed Computer Vision and Pattern Recognition The task of grading atopic dermatitis (or AD, a form of eczema) from patient images is difficult even for trained dermatologists. Research on automating this task has progressed in recent years with the development of deep learning solutions; however, the rapid evolution of multimodal models and more specifically vision-language models (VLMs) opens the door to new possibilities in terms of explainable assessment of medical images, including dermatology. This report describes experiments carried out to evaluate the ability of seven VLMs to assess the severity of AD on a set of test images. |
| title | VLM Models and Automated Grading of Atopic Dermatitis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.17835 |