VICCA: Visual Interpretation and Comprehension of Chest X-ray Anomalies in Generated Report Without Human Feedback

• URL: http://arxiv.org/abs/2501.17726v1
• Date: Wed, 29 Jan 2025 16:02:16 GMT
• Title: VICCA: Visual Interpretation and Comprehension of Chest X-ray Anomalies in Generated Report Without Human Feedback
• Authors: Sayeh Gholipour Picha, Dawood Al Chanti, Alice Caplier,
• Abstract summary: We propose a novel framework designed to enhance the semantic alignment and localization accuracy of AI-generated medical reports. By comparing features between the original and generated images, we introduce a dual-scoring system. This approach significantly outperforms existing methods, achieving state-of-the-art results in pathology localization and text-to-image alignment.
• Score: 1.5839621757142595
• License:
• Abstract: As artificial intelligence (AI) becomes increasingly central to healthcare, the demand for explainable and trustworthy models is paramount. Current report generation systems for chest X-rays (CXR) often lack mechanisms for validating outputs without expert oversight, raising concerns about reliability and interpretability. To address these challenges, we propose a novel multimodal framework designed to enhance the semantic alignment and localization accuracy of AI-generated medical reports. Our framework integrates two key modules: a Phrase Grounding Model, which identifies and localizes pathologies in CXR images based on textual prompts, and a Text-to-Image Diffusion Module, which generates synthetic CXR images from prompts while preserving anatomical fidelity. By comparing features between the original and generated images, we introduce a dual-scoring system: one score quantifies localization accuracy, while the other evaluates semantic consistency. This approach significantly outperforms existing methods, achieving state-of-the-art results in pathology localization and text-to-image alignment. The integration of phrase grounding with diffusion models, coupled with the dual-scoring evaluation system, provides a robust mechanism for validating report quality, paving the way for more trustworthy and transparent AI in medical imaging.

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