面向个性化自动疼痛评估的少样本学习

中文摘要 AI 辅助

疼痛感知在不同个体间存在显著差异，这使得基于群体的分类器难以在数据集中的所有受试者上泛化。考虑受试者变异性的一个方法是训练个性化分类器。在本工作中，我们评估了少样本学习（元学习的一个子领域）作为自动疼痛评估中个性化方法的应用。我们将从群体层面评估到个体层面评估的转变重新解释为任务域转移，其中观察到的类别保持不变，但目标受试者发生变化。我们在BioVid疼痛数据库、SenseEmotion数据库和PainMonit实验数据集（PMED）上评估了我们的方法，在留一受试者交叉验证协议下，在二分类和多分类设置中，BioVid上分别达到85.75%和35.49%的准确率，SenseEmotion上分别达到82.37%和41.88%的准确率，PMED上达到90.47%的准确率（PMED仅有二分类基准）。使用样本进行k-shot条件设置，准确率分别提升至86.25%、40.06%、83.43%、44.08%和91.25%。为进一步评估我们方法的效果和鲁棒性，我们提供了额外的消融实验并研究了个性化效果。我们的结果表明，支持条件化的少样本适应可以在受试者间变异性下提高平均性能。

英文摘要

Pain perception varies substantially across individuals, making it difficult for population-based classifiers to generalise across all subjects in a dataset. One way to account for subject variability is to train personalised classifiers. In this work, we evaluate Few-Shot Learning, a sub-area of Meta-Learning, as an approach to personalisation in automated pain assessment. We re-interpret the shift from population-level to subject-level evaluation as a task-domain shift, where the observed classes remain fixed but the target subject changes. We evaluate our method on the BioVid Pain Database, the SenseEmotion Database, and the PainMonit Experimental Dataset (PMED), reaching 85.75% and 35.49% accuracy on BioVid and 82.37% and 41.88% on SenseEmotion in the binary and multi-class settings under a Leave-One-Subject-Out CV protocol respectively, and 90.47% on PMED, for which only a binary benchmark exists. Using samples to implement k-shot conditioning, the accuracies can be improved to 86.25%, 40.06%, 83.43%, 44.08%, and 91.25%, respectively. To further evaluate the effects and robustness of our method, we provide additional ablation experiments and investigate the personalisation effects. Our results suggest that support-conditioned few-shot adaptation can improve average performance under inter-subject variability.

发表机构

• IU International University of Applied Sciences（IU国际应用科学大学）
• Ulm University（乌尔姆大学）

机构由 AI 辅助整理，请以论文原文为准。