SentZero pairs abstract-level sentence mapping with patch-level false-negative alignment to beat prior multi-task zero-shot methods on chest X-ray classification and grounding after MIMIC-CXR pretraining
SentZero is a sentence-centric vision-language pretraining framework for chest X-ray: it uses an LLM to extract phrases from radiology reports and map them to concise topic-presence sentences that expand positive-pair diversity, adds an auxiliary loss that attracts the highest-similarity image patches toward clinical sentences recurring across studies to mitigate false negatives, and applies sentence-conditioned residual modulation to visual embeddings; after pretraining on MIMIC-CXR it outperforms prior multi-task zero-shot methods on most metrics for zero-shot multi-label classification on Open-I, ChestXray14, PadChest, ChestXDet10 and CheXpert and for zero-shot grounding on ChestXDet10 and MS-CXR.