A Quantitative Evaluation Framework for Temporal Explainability in Echocardiographic Video Segmentation

2026-09-07

AI Summary

本文提出一种定量框架，通过四个指标评估超声心动图视频分割中的时间可解释性问题，使用Grad-CAM方法对比了2D U-Net和ConvLSTM U-Net模型。

Abstract

Deep learning has achieved state-of-the-art performance in echocardiographic video segmentation, with an increasing number of models incorporating temporal information. However, quantitative evaluation of temporal explainability remains largely unexplored. We propose a quantitative framework for evaluating Grad-CAM explanations using four complementary metrics measuring temporal consistency, saliency motion, anatomical overlap, and temporal overlap. Using EchoNet-Dynamic, we compare a baseline 2D U-Net with ConvLSTM U-Net models trained across multiple temporal strides. While segmentation performance remained comparable across all models, intermediate ConvLSTM explanations exhibited substantially lower saliency consistency and greater centroid motion than final prediction explanations. Temporal Bottleneck explanations were significantly more stable than Encoder Bottleneck explanations across all strides, while final ConvLSTM Decoder3 explanations were broadly comparable to those of the 2D U-Net. Importantly, conventional frame-wise explanation metrics cannot determine whether variation in intermediate explanations reflects meaningful temporal feature evolution or explanation instability. These findings establish a preliminary quantitative framework for temporal explainability and motivate temporal-aware XAI methods that explicitly account for evolving representations in medical video models.

Problem

• Temporal Explainability
• Echocardiographic Video Segmentation
• Quantitative Evaluation

Innovation

• Temporal Explainability
• Quantitative Evaluation Framework
• Grad-CAM Explanations
• Echocardiographic Video Segmentation