用强化学习智能选超声片段，少用70%数据仍更准
Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification
- 用强化学习决定何时停止分析，动态选择最优片段子集
- 仅用30%片段即达AUC 0.91，优于全量片段和基准方法
- 适合追求高效准确的临床影像诊断系统开发者
胸超声检查需采集多个心脏视角的视频片段，导致数据量庞大。传统自动方法或仅使用单个片段，忽略其他互补信息；或平均所有片段预测结果，计算成本高，难用于临床。本文提出一种基于强化学习的方法，让智能体学习在分类不确定性降低前持续处理片段，或在置信度足够时提前终止。同时设计可学习的注意力融合机制，灵活整合多片段信息。实验表明，该方法仅使用全部片段的30%即可实现0.91的AUC，在心肌淀粉样变性检测任务中超越全量片段及多种基准方法。
原文摘要 · Abstract (English)
Guidelines for transthoracic echocardiographic examination recommend the acquisition of multiple video clips from different views of the heart, resulting in a large number of clips. Typically, automated methods, for instance disease classifiers, either use one clip or average predictions from all clips. Relying on one clip ignores complementary information available from other clips, while using all clips is computationally expensive and may be prohibitive for clinical adoption. To select the optimal subset of clips that maximize performance for a specific task (image-based disease classification), we propose a method optimized through reinforcement learning. In our method, an agent learns to either keep processing view-specific clips to reduce the disease classification uncertainty, or stop processing if the achieved classification confidence is sufficient. Furthermore, we propose a learnable attention-based aggregation method as a flexible way of fusing information from multiple clips. The proposed method obtains an AUC of 0.91 on the task of detecting cardiac amyloidosis using only 30% of all clips, exceeding the performance achieved from using all clips and from other benchmarks.
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