针对糖尿病足神经病变识别，提出多子源域自适应方法提升模型泛化能力。
MSSDA: Multi-Sub-Source Adaptation for Diabetic Foot Neuropathy Recognition
- 按卷积特征统计划分子源域，避免领域差异过大
- 在特定特征空间对齐源与目标域分布，缩小领域差距
- 在新构建数据集和现有数据集上均表现优异
糖尿病足神经病变（DFN）是导致糖尿病足溃疡的关键因素，而糖尿病足溃疡是糖尿病最常见且严重的并发症之一，与截肢和死亡风险密切相关。尽管其重要性突出，现有数据集未直接基于足底压力数据，缺乏连续、长期的足部特异性信息。为推动DFN研究，我们收集了一个包含连续足底压力数据的新数据集，涵盖94名患有DFN的糖尿病患者及41名无DFN的糖尿病患者。传统方法按个体划分数据集，可能因缺少中间域数据导致某些特征空间存在显著领域差异。本文提出一种有效的域自适应方法：基于卷积特征统计划分子源域，选择合适子源域以提升效率并避免负迁移；随后在特定特征空间中对齐每对源-目标域的分布，以最小化领域差距。大量实验结果验证了该方法在新提出的DFN识别数据集及现有数据集上的有效性。
原文摘要 · Abstract (English)
Diabetic foot neuropathy (DFN) is a critical factor leading to diabetic foot ulcers, which is one of the most common and severe complications of diabetes mellitus (DM) and is associated with high risks of amputation and mortality. Despite its significance, existing datasets do not directly derive from plantar data and lack continuous, long-term foot-specific information. To advance DFN research, we have collected a novel dataset comprising continuous plantar pressure data to recognize diabetic foot neuropathy. This dataset includes data from 94 DM patients with DFN and 41 DM patients without DFN. Moreover, traditional methods divide datasets by individuals, potentially leading to significant domain discrepancies in some feature spaces due to the absence of mid-domain data. In this paper, we propose an effective domain adaptation method to address this proplem. We split the dataset based on convolutional feature statistics and select appropriate sub-source domains to enhance efficiency and avoid negative transfer. We then align the distributions of each source and target domain pair in specific feature spaces to minimize the domain gap. Comprehensive results validate the effectiveness of our method on both the newly proposed dataset for DFN recognition and an existing dataset.
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