<ul><li>RadZero is a similarity-based cross-attention framework designed for vision-language alignment in radiology with zero-shot multi-task capability.</li><li>It addresses the challenges of effectively utilizing complex radiology reports, relying on low-resolution images, and limited interpretability in attention mechanisms.</li><li>RadZero leverages large language models to extract semantic sentences from radiology reports and employs a multi-positive contrastive learning strategy to capture relationships between images and textual descriptions.</li><li>Experimental results show that RadZero outperforms state-of-the-art methods in zero-shot classification, grounding, segmentation, and improves explainability in vision-language alignment.</li></ul>

RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability

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