通过多阶段协作学习联合增强胸部X线自动诊断与放射科医生视线预测
Joint enhancement of automatic chest X-ray diagnosis and radiological gaze prediction with multi-stage cooperative learning
AI总结：
提出双编码器多任务UNet与多阶段协作学习策略，联合完成胸部X线疾病诊断和临床视觉注意图预测，性能显著优于现有方法。
摘要：
目的：由于视觉检查是放射学筛查过程中的固有环节，相关的眼动视线数据能够为相关临床决策提供有价值的洞察。随着深度学习成为计算机辅助诊断的最先进方法，将眼动视线数据等人类行为整合到这些系统中，有助于使机器预测与临床诊断标准保持一致，从而提升放射学自动诊断的质量。方法：我们提出了一种新颖的深度学习框架，用于胸部X线扫描的疾病诊断以及相应临床视觉注意图预测。具体而言，我们引入了一种新的双编码器多任务UNet，该网络同时利用DenseNet201主干网络和基于残差与Squeeze-and-Excitation模块的编码器来提取多样化特征，用于视觉注意图预测，并采用多尺度特征融合分类器进行疾病分类。为解决多任务学习中各任务训练进度不同步的问题，我们提出了一种多阶段协作学习策略，并通过对比学习进行特征编码器预训练以提升性能。结果：所提方法在胸部X线诊断（AUC=0.93）和视觉注意图预测质量（相关系数=0.58）方面均显著优于现有技术。结论：得益于所提出的多任务多阶段协作学习，我们的技术证明了将临床医生的眼动视线整合到临床AI系统中的价值，可提升性能并可能增强可解释性。
英文摘要：
Purpose: As visual inspection is an inherent process during radiological screening, the associated eye gaze data can provide valuable insights into relevant clinical decisions. As deep learning has become the state-of-the-art for computer-assisted diagnosis, integrating human behavior, such as eye gaze data, into these systems is instrumental to help align machine predictions with clinical diagnostic criteria, thus enhancing the quality of automatic radiological diagnosis. Methods: We propose a novel deep learning framework for joint disease diagnosis and prediction of corresponding clinical visual attention maps for chest X-ray scans. Specifically, we introduce a new dual-encoder multi-task UNet, which leverages both a DenseNet201 backbone and a Residual and Squeeze-and-Excitation block-based encoder to extract diverse features for visual attention map prediction, and a multi-scale feature-fusion classifier to perform disease classification. To tackle the issue of asynchronous training schedules of individual tasks in multi-task learning, we proposed a multi-stage cooperative learning strategy, with contrastive learning for feature encoder pretraining to boost performance. Results: Our proposed method is shown to significantly outperform existing techniques for chest X-ray diagnosis (AUC=0.93) and the quality of visual attention map prediction (Correlation coefficient=0.58). Conclusion: Benefiting from the proposed multi-task multi-stage cooperative learning, our technique demonstrates the benefit of integrating clinicians' eye gaze into clinical AI systems to boost performance and potentially explainability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
