用频域随机丢弃提升医学图像分割的不确定性估计精度
Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout
- 在频域而非空间域应用随机丢弃，生成多样纹理特征
- 在三种医学影像任务中显著改善预测校准与边界分割
- 适合需高可信度分割结果的医疗诊断场景
蒙特卡洛（MC）丢弃为确定性神经网络提供了实用的预测分布估计方法。传统丢弃在信号空间中应用，难以捕捉医学影像中常见的频率相关噪声，导致预测估计偏差。本文提出一种新方法，将丢弃扩展至频域，在推理时对信号频率进行随机衰减，生成多样化的全局纹理变化，同时保持结构完整性——我们假设并实证其有助于更准确地估计语义分割中的不确定性。我们在三项不同成像模态的分割任务中评估了传统MC丢弃与MC频域丢弃：(i) 双参数MRI中的前列腺分区，(ii) 增强CT中的肝肿瘤，(iii) 胸部X光片中的肺部。结果表明，MC频域丢弃提升了模型校准度、收敛性及语义不确定性估计，增强了预测可靠性、边界划分能力，有潜力提升医疗决策质量。
原文摘要 · Abstract (English)
Monte-Carlo (MC) Dropout provides a practical solution for estimating predictive distributions in deterministic neural networks. Traditional dropout, applied within the signal space, may fail to account for frequency-related noise common in medical imaging, leading to biased predictive estimates. A novel approach extends Dropout to the frequency domain, allowing stochastic attenuation of signal frequencies during inference. This creates diverse global textural variations in feature maps while preserving structural integrity -- a factor we hypothesize and empirically show is contributing to accurately estimating uncertainties in semantic segmentation. We evaluated traditional MC-Dropout and the MC-frequency Dropout in three segmentation tasks involving different imaging modalities: (i) prostate zones in biparametric MRI, (ii) liver tumors in contrast-enhanced CT, and (iii) lungs in chest X-ray scans. Our results show that MC-Frequency Dropout improves calibration, convergence, and semantic uncertainty, thereby improving prediction scrutiny, boundary delineation, and has the potential to enhance medical decision-making.
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