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TAP-JEPA is introduced, a ViT-B/3D trained on 113,611 CT volumes that is competitive with state-of-the-art encoders in scan-level classification and shows that contextual predictability is not a sufficient proxy for clinical relevance in medical representation learning.
Purpose: To develop and evaluate an open-weight large language model (LLM) pipeline that converts an entire archive of free-text radiology reports into structured reports without human oversight. Materials and Methods: In this retrospective study, a pipeline with 150 hierarchically organized templates was developed at...
RadPRISM makes a clinician-defined radiology schema a designated stratification axis: an on-premise large language model extracts per-concept text spans from free-text reports, and each clinical concept is aligned in its own dedicated visual subspace, turning concept stratification into direct, top-level alignment supe...
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