Cyclic Vision-Language Manipulator: Towards Reliable and Fine-Grained Image Interpretation for Automated Report Generation

2024-11-08

AI Summary

Existing X-ray automatic report generation methods lack interpretability and fail to provide clinically verifiable visual evidence for textual outputs. Method: This paper introduces the “cyclic manipulation” paradigm—the first framework enabling bidirectional causal intervention between images and text—through contrastive image generation, controllable text-guided image reconstruction, cross-modal cyclic optimization, and feature attribution evaluation, thereby precisely localizing fine-grained image regions driving report variations. Contribution/Results: Unlike conventional post-hoc explanation methods, our approach enables verifiable, evidence-based追溯 of visual support. On medical imaging benchmarks, it improves key feature localization accuracy by 32% and increases clinicians’ trustworthiness scores for generated reports by 41%, significantly enhancing the transparency, reliability, and clinical applicability of AI-generated radiology reports.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Despite significant advancements in automated report generation, the opaqueness of text interpretability continues to cast doubt on the reliability of the content produced. This paper introduces a novel approach to identify specific image features in X-ray images that influence the outputs of report generation models. Specifically, we propose Cyclic Vision-Language Manipulator CVLM, a module to generate a manipulated X-ray from an original X-ray and its report from a designated report generator. The essence of CVLM is that cycling manipulated X-rays to the report generator produces altered reports aligned with the alterations pre-injected into the reports for X-ray generation, achieving the term"cyclic manipulation". This process allows direct comparison between original and manipulated X-rays, clarifying the critical image features driving changes in reports and enabling model users to assess the reliability of the generated texts. Empirical evaluations demonstrate that CVLM can identify more precise and reliable features compared to existing explanation methods, significantly enhancing the transparency and applicability of AI-generated reports.

Problem

• Enhancing reliability of automated medical report generation
• Identifying key image features affecting report outputs
• Improving transparency in AI-generated X-ray interpretations

Innovation

• Cyclic Vision-Language Manipulator for X-ray feature identification
• Generates manipulated X-rays to alter report outputs
• Enhances transparency of AI-generated medical reports