用信号强度加权坐标，提升生物医学信号定位的稳定性与泛化能力
Signal Intensity-weighted coordinate channels improve learning stability and generalisation in 1D and 2D CNNs in localisation tasks on biomedical signals
- 输入层坐标通道改用局部信号强度加权，引入强度-位置耦合先验
- 在心电图和细胞图像上均实现更快收敛与更高精度，跨模态有效
- 适合处理复杂强度分布的生物医学信号定位任务
生物医学信号定位任务常需模型从具有复杂强度分布的信号中学习有意义的空间或时间关系。现有方法如CoordConv通过在卷积输入中添加坐标通道，使网络能够学习绝对位置。本文提出一种信号强度加权的坐标表示，将纯坐标通道替换为由局部信号强度缩放的通道，直接在输入表示中嵌入强度-位置耦合机制，引入一种简单且模态无关的归纳偏置。我们在两个不同定位任务上评估该方法：(i) 预测20秒双导联心电信号中的形态转变时间；(ii) 在SiPaKMeD数据集的细胞图像中回归核中心坐标。结果表明，相较于传统坐标通道方法，该表示在两种情况下均实现更快收敛与更高泛化性能，验证了其在一维与二维生物医学信号中的有效性。
原文摘要 · Abstract (English)
Localisation tasks in biomedical data often require models to learn meaningful spatial or temporal relationships from signals with complex intensity distributions. A common strategy, exemplified by CoordConv layers, is to append coordinate channels to convolutional inputs, enabling networks to learn absolute positions. In this work, we propose a signal intensity-weighted coordinate representation that replaces the pure coordinate channels with channels scaled by local signal intensity. This modification embeds an intensity-position coupling directly in the input representation, introducing a simple and modality-agnostic inductive bias. We evaluate the approach on two distinct localisation problems: (i) predicting the time of morphological transition in 20-second, two-lead ECG signals, and (ii) regressing the coordinates of nuclear centres in cytological images from the SiPaKMeD dataset. In both cases, the proposed representation yields faster convergence and higher generalisation performance relative to conventional coordinate-channel approaches, demonstrating its effectiveness across both one-dimensional and two-dimensional biomedical signals.
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