用可解释的动态原型学习，让超声心动图射血分数预测更准确且透明。
ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography
- 基于视频原型学习，捕捉心脏运动的关键模式。
- 在EchonetDynamic数据集上达到79.64% F1分数，性能媲美黑箱模型。
- 适合需要可解释性医疗AI的临床医生与研究者。
射血分数（EF）是评估心脏功能和诊断心力衰竭的关键指标。传统方法依赖人工勾画和专业经验，耗时且存在观察者差异。现有深度学习方法多为黑箱模型，缺乏透明性，降低临床信任度。虽有事后可解释性方法，但无法引导模型内部推理，可靠性有限。为此，我们提出ProtoEFNet，一种基于视频的原型学习模型，用于连续EF回归。该模型学习动态时空原型，捕捉临床相关的心脏运动模式，并引入原型角度分离（PAS）损失，强化连续EF范围内的判别性表征。在EchonetDynamic数据集上的实验表明，ProtoEFNet性能与非可解释模型相当，同时提供临床可理解的洞察。消融实验证明，该损失使F1分数从77.67±2.68提升至79.64±2.10。源代码已开源。
原文摘要 · Abstract (English)
Ejection fraction (EF) is a crucial metric for assessing cardiac function and diagnosing conditions such as heart failure. Traditionally, EF estimation requires manual tracing and domain expertise, making the process time-consuming and subject to interobserver variability. Most current deep learning methods for EF prediction are black-box models with limited transparency, which reduces clinical trust. Some post-hoc explainability methods have been proposed to interpret the decision-making process after the prediction is made. However, these explanations do not guide the model's internal reasoning and therefore offer limited reliability in clinical applications. To address this, we introduce ProtoEFNet, a novel video-based prototype learning model for continuous EF regression. The model learns dynamic spatiotemporal prototypes that capture clinically meaningful cardiac motion patterns. Additionally, the proposed Prototype Angular Separation (PAS) loss enforces discriminative representations across the continuous EF spectrum. Our experiments on the EchonetDynamic dataset show that ProtoEFNet can achieve accuracy on par with its non-interpretable counterpart while providing clinically relevant insight. The ablation study shows that the proposed loss boosts performance with a 2% increase in F1 score from 77.67$\pm$2.68 to 79.64$\pm$2.10. Our source code is available at: https://github.com/DeepRCL/ProtoEF
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。