titleSentZero: An Enhanced Sentence-Centric Vision-Language Pretraining for Multi-Task Zero-Shot Chest X-Ray Analysis
authorsHangyul Yoon, Hyungyung Lee, Edward Choi, Eunho Yang
date2026-09-28
venuearXiv (preprint, not peer reviewed)
idarXiv:2609.34479
upvotes6 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractVision-language (VL) pretraining using paired chest X-ray (CXR) images and radiology reports has shown strong potential for medical image understanding. However, existing methods often remain dependent on task-specific finetuning because radiology reports are lengthy, clinically dense, and difficult to align with simple zero-shot prompts. Recent sentence-level approaches partially address this limitation using clinical phrases extracted by large language models (LLMs), but they largely overlook the intrinsic characteristics of radiology discourse. In particular, limited positive-pair diversity constrains further gains, while clinically equivalent sentences frequently recur across patients, creating false negatives in contrastive learning. To address these issues, we propose SentZero, an enhanced sentence-centric VL pretraining framework for zero-shot, multi-task CXR analysis. SentZero introduces LLM-based abstract-level sentence structuring and mapping to expand positive-pair diversity, together with an additional loss term to mitigate false negatives. We further introduce sentence-conditioned residual modulation of visual embeddings, enabling visual features to adapt to the semantic characteristics of each input sentence. Across diverse downstream tasks and datasets, SentZero improves zero-shot generalization and outperforms prior multi-task zero-shot methods.

cite

@misc{yoon2026sentzero,
  title={SentZero: An Enhanced Sentence-Centric Vision-Language Pretraining for Multi-Task Zero-Shot Chest X-Ray Analysis},
  author={Hangyul Yoon and Hyungyung Lee and Edward Choi and Eunho Yang},
  year={2026},
  eprint={2609.34479},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2609.34479}
}