用注意力机制剪裁胸片，大幅压缩数据仍保诊断精度
NEURAL: Attention-Guided Pruning for Unified Multimodal Resource-Constrained Clinical Evaluation
- 用视觉语言模型的交叉注意力得分定位关键病灶区域
- 图像压缩率达93.4%-97.7%，肺炎检测AUC达0.88-0.95
- 适合资源受限场景下的远程影像诊断与高效数据存储
多模态医学影像数据的快速增长给存储和传输带来挑战，尤其在资源受限的临床环境中。本文提出NEURAL框架，通过语义引导的数据压缩解决该问题。该方法复用微调后的生成式视觉-语言模型中图像与放射报告间的交叉注意力分数，对胸部X光进行结构化剪裁，仅保留具有诊断意义的区域，生成高度压缩的图表示。这一统一的图结构将剪裁后的视觉图与来自临床报告的知识图融合，形成通用数据形式，简化下游建模。在MIMIC-CXR和CheXpert Plus数据集上验证，图像数据量减少93.4%-97.7%，同时保持0.88-0.95的高诊断性能（AUC），优于使用未压缩数据的基线模型。通过构建持久、任务无关的数据资产，NEURAL解决了数据大小与临床效用之间的权衡，实现高效工作流与远程放射学，且不牺牲性能。代码已开源：https://github.com/basiralab/NEURAL。
原文摘要 · Abstract (English)
The rapid growth of multimodal medical imaging data presents significant storage and transmission challenges, particularly in resource-constrained clinical settings. We propose NEURAL, a novel framework that addresses this by using semantics-guided data compression. Our approach repurposes cross-attention scores between the image and its radiological report from a fine-tuned generative vision-language model to structurally prune chest X-rays, preserving only diagnostically critical regions. This process transforms the image into a highly compressed, graph representation. This unified graph-based representation fuses the pruned visual graph with a knowledge graph derived from the clinical report, creating a universal data structure that simplifies downstream modeling. Validated on the MIMIC-CXR and CheXpert Plus dataset for pneumonia detection, NEURAL achieves a 93.4-97.7\% reduction in image data size while maintaining a high diagnostic performance of 0.88-0.95 AUC, outperforming other baseline models that use uncompressed data. By creating a persistent, task-agnostic data asset, NEURAL resolves the trade-off between data size and clinical utility, enabling efficient workflows and teleradiology without sacrificing performance. Our NEURAL code is available at https://github.com/basiralab/NEURAL.
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