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A Vision-Language Foundation Model for Zero-shot Clinical Collaboration and Automated Concept Discovery in Dermatology
Siyuan Yan, Xieji Li, Dan Mo +28
Medical foundation models have shown promise in controlled benchmarks, yet widespread deployment remains hindered by reliance on task-specific fine-tuning. Here, we introduce DermF…
Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology
Siyuan Yan, Ming Hu, Yiwen Jiang +5
The emergence of vision-language models has transformed medical AI, enabling unprecedented advances in diagnostic capability and clinical applications. However, progress in dermato…
A Multimodal Vision Foundation Model for Clinical Dermatology
Siyuan Yan, Zhen Yu, Clare Primiero +22
Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep l…
Automated dermatoscopic pattern discovery by clustering neural network output for human-computer interaction
Lidia Talavera-Martinez, Philipp Tschandl
Background: As available medical image datasets increase in size, it becomes infeasible for clinicians to review content manually for knowledge extraction. The objective of this st…
The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
Amirreza Mahbod, Philipp Tschandl, Georg Langs +2
Malignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin l…
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
Noel Codella, Veronica Rotemberg, Philipp Tschandl +9
This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that…