Abstract
Explainable artificial intelligence (AI) techniques are increasingly being explored to provide insights into why AI and machine learning (ML) models provide a certain outcome in various applications. However, there has been limited exploration of explainable AI techniques on time-series data, especially in the healthcare context. In this paper, we describe a threshold-based method that utilizes a weakly supervised model and a gradient-based explainable AI technique (i.e. saliency map) and explore its feasibility to identify salient frames of time-series data. Using the dataset from 15 post-stroke survivors performing three upper-limb exercises and labels on whether a compensatory motion is observed or not, we implemented a feed-forward neural network model and utilized gradients of each input on model outcomes to identify salient frames that involve compensatory motions. According to the evaluation using frame-level annotations, our approach achieved a recall of 0.96 and an F2-score of 0.91. Our results demonstrated the potential of a gradient-based explainable AI technique (e.g. saliency map) for time-series data, such as highlighting the frames of a video that therapists should focus on reviewing and reducing the efforts on frame-level labeling for model training.
Abstract (translated)
解释性人工智能(AI)技术正在 increasingly 探索，以提供对 AI 和机器学习(ML)模型在多种应用中提供特定结果的洞察。然而，对时间序列数据的解释性 AI 技术的有限探索，特别是在医疗context中。在本文中，我们描述了一种基于阈值的方法，该方法利用一个弱监督模型和一个基于梯度的解释性 AI 技术(即响应图)，并探索了确定时间序列数据中的突出帧的可行性。使用15个中风康复者进行三头肌锻炼的数据集以及是否观察到补偿运动的标签，我们实现了一个前馈神经网络模型，并利用每个输入的梯度来确定涉及补偿运动的突出帧。根据使用帧级别的注释进行评估，我们的方法实现了0.96召回率和0.91F2得分。我们的结果证明了基于梯度的解释性 AI 技术(如响应图)对时间序列数据的的潜力，例如强调视频帧中治疗师应该重点审查的内容，并减少用于模型训练帧级别的注释工作的努力。
URL
https://arxiv.org/abs/2305.05525
