AlphaRad: Grounded Zero-Shot Classification in Chest Radiology via α-Corrected Binary Cross Entropy and Factorized Latent Supervision

Vision-Language Pretrained Models (VLPMs) offer a scal-able path to open-vocabulary chest radiology understanding, yet two as-pects remain underexplored: how structured clinical semantics extractedfrom medical reports can reduce in-batch noise during contrastive learn-ing, and how cross-modal fusion can be designed to produce more faithfulspatial grounding without added complexity. We introduce AlphaRad,addressing these opportunities through two contributions. First, we con-struct a large-scale structured medical concept space from medical re-ports parsed by a Large Language Model for training, thereby mit-igating in-batch learning noise and removing heuristic pair matchingin contrastive learning, and thus naturally positioning AlphaRad asa medical concept discriminator trained via α-Corrected Binary Cross-Entropy. Second, we propose FLaS (Factorized Latent Supervision), anextremely simple yet effective cross-modal feature fusion module thatfactorizes VLPM representations into independent subspaces, using ded-icated alignment supervision to enhance the expressiveness of spatialgrounding without introducing additional model parameters. Throughextensive empirical validation, AlphaRad shows strong zero-shot gen-eralization across diverse chest radiology tasks. Notably, it establishesstate-of-the-art average performance across 16 classification benchmarks,while achieving individual state-of-the-art results via distinct gains on 7grounding/phrase grounding and 3 segmentation datasets.