Siyuan Yan

DermLIP is a vision-language model for dermatology, trained on the Derm1M dataset—the largest dermatological image-text corpus to date. python import openclip from PIL import Image import torch model,, preprocess = openclip.createmodelandtransforms( 'hf-hub:redlessone/DermLIPViT-B-16' model.eval() tokenizer = openclip.gettokenizer('hf-hub:redlessone/DermLIPViT-B-16') image = preprocess(Image.open("yourskinimage.png")).unsqueeze(0) PADCLASSNAMES = [ "nevus", "basal cell carcinoma", "actinic keratosis", "seborrheic keratosis", "squamous cell carcinoma", "melanoma" template = lambda c: f'This is a skin image of {c}' text = tokenizer([template(c) for c in PADCLASSNAMES]) with torch.nograd()…

DermFM-Zero is a dermatology vision–language foundation model. It pairs a PanDerm ViT-L/16 vision encoder with native-resolution input (NaViT patch-and-pack) and a PubMedBERT-256 text encoder pretrained on a Derm1M knowledge tree (KEP), aligned on 517,455 public dermatology image–text pairs with multi-aspect knowledge contrastive learning (MAKE) and knowledge distillation. It performs zero-shot diagnosis and cross-modal retrieval, serves as a backbone for multimodal fine-tuning and VQA, and yields interpretable concepts through sparse autoencoders. Across extensive benchmarks and three multinational reader studies, DermFM-Zero achieves state-of-the-art zero-shot performance while improving…