69 citations · 93 across the 14 of their papers we have counts for
4 papers · 2 filters
DermaVQA-DAS: Dermatology Assessment Schema (DAS) & Datasets for Closed-Ended Question Answering & Segmentation in Patient-Generated Dermatology Images
Wen-wai Yim, Yujuan Fu, Asma Ben Abacha +4
Recent advances in dermatological image analysis have been driven by large-scale annotated datasets; however, most existing benchmarks focus on dermatoscopic images and lack patien…
Comprehensive language-image pre-training for 3D medical image understanding
Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao +13
In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abn…
Data Scaling Laws for Radiology Foundation Models
Maximilian Ilse, Harshita Sharma, Anton Schwaighofer +12
Foundation vision encoders such as CLIP and DINOv2, trained on web-scale data, exhibit strong transfer performance across tasks and datasets. However, medical imaging foundation mo…
From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification
Xue Li, Jameson Merkow, Noel C. F. Codella +11
Foundation models provide robust embeddings for diverse tasks, including medical imaging. We evaluate embeddings from seven general and medical-specific foundation models (e.g., De…