用多任务学习融合听诊音、病史和患者信息，提升呼吸系统疾病诊断准确率
Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis
- 设计三重多任务学习框架，联合建模听诊音、疾病表现和患者元数据
- 在多个公开数据集上实现听诊音分类与疾病诊断性能显著提升
- 适合医疗AI研究者及临床辅助诊断系统开发者参考
听诊仍是临床实践的核心，对初步评估和持续监测至关重要。医生通过结合患者病史和检查结果，聆听肺部声音做出诊断。基于此强关联性，多任务学习（MTL）可为同时建模呼吸音模式与疾病表现提供有力框架。尽管MTL在医学应用中展现出巨大潜力，但呼吸音、疾病表现与患者元数据之间的复杂交互关系仍缺乏深入研究。本研究探索将先进深度学习架构与MTL结合，以提升呼吸音分类与疾病诊断能力。特别地，我们扩展了元数据对呼吸音分类有益的最新发现，评估其在MTL框架中的有效性。全面实验表明，在将听诊信息纳入MTL架构后，呼吸音分类与诊断性能均取得显著提升。
原文摘要 · Abstract (English)
Auscultation remains a cornerstone of clinical practice, essential for both initial evaluation and continuous monitoring. Clinicians listen to the lung sounds and make a diagnosis by combining the patient's medical history and test results. Given this strong association, multitask learning (MTL) can offer a compelling framework to simultaneously model these relationships, integrating respiratory sound patterns with disease manifestations. While MTL has shown considerable promise in medical applications, a significant research gap remains in understanding the complex interplay between respiratory sounds, disease manifestations, and patient metadata attributes. This study investigates how integrating MTL with cutting-edge deep learning architectures can enhance both respiratory sound classification and disease diagnosis. Specifically, we extend recent findings regarding the beneficial impact of metadata on respiratory sound classification by evaluating its effectiveness within an MTL framework. Our comprehensive experiments reveal significant improvements in both lung sound classification and diagnostic performance when the stethoscope information is incorporated into the MTL architecture.
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