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Learning Sparse Visual Representations via Spatial-Semantic Factorization
Theodore Zhengde Zhao, Sid Kiblawi, Jianwei Yang +6
Self-supervised learning (SSL) faces a fundamental conflict between semantic understanding and image reconstruction. High-level semantic SSL (e.g., DINO) relies on global tokens th…
Comprehensive language-image pre-training for 3D medical image understanding
Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao +14
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…
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…
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…
Exploring scalable medical image encoders beyond text supervision
Fernando Pérez-GarcÃa, Harshita Sharma, Sam Bond-Taylor +12
Language-supervised pre-training has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal sys…