AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via $\alpha$-Corrected Binary Cross Entropy and Factorized Latent Supervision

AlphaRAD introduces a grounded zero‑shot classification framework for chest radiology that leverages structured medical concepts extracted from reports and a novel α‑Corrected Binary Cross‑Entropy loss to reduce in‑batch noise. It also presents FLaS, a lightweight cross‑modal fusion module that factorizes VLPM representations into independent subspaces, improving spatial grounding without adding parameters. The method achieves state‑of‑the‑art performance on 16 classification benchmarks and sets new records on several grounding, phrase‑grounding, and segmentation datasets.

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• llms
• computer-vision
• multimodal
• benchmarks
• safety