用谱归一化与高斯过程提升病理图像分类的不确定性感知能力
Uncertainty-Aware Image Classification In Biomedical Imaging Using Spectral-normalized Neural Gaussian Processes
- 引入谱归一化和高斯过程层，增强单模型不确定性估计
- 在6个数据集上显著提升分布外检测能力，保持分类精度
- 适合需要可信决策的临床病理分析场景
精准的组织病理学判读对临床决策至关重要；然而，当前数字病理学中的深度学习模型在分布外（OOD）情形下常表现过度自信且校准不佳，限制了其可信度与临床应用。安全关键的医学影像流程需要具备内在不确定性感知能力的模型，以准确拒绝分布外输入。本文采用谱归一化神经高斯过程（SNGP），通过谱归一化及将最后全连接层替换为高斯过程层，实现轻量级改进，提升单模型的不确定性估计与分布外检测性能。我们在白细胞、淀粉样斑块和结直肠病理三个任务的六个数据集上对比了SNGP与确定性模型及蒙特卡洛丢弃法。结果表明，SNGP在分布内性能相当的同时，显著改善了不确定性估计与分布外检测能力。因此，SNGP或相关模型为数字病理中的不确定性感知分类提供了有效框架，支持安全部署并增强病理科医生信任。
原文摘要 · Abstract (English)
Accurate histopathologic interpretation is key for clinical decision-making; however, current deep learning models for digital pathology are often overconfident and poorly calibrated in out-of-distribution (OOD) settings, which limit trust and clinical adoption. Safety-critical medical imaging workflows benefit from intrinsic uncertainty-aware properties that can accurately reject OOD input. We implement the Spectral-normalized Neural Gaussian Process (SNGP), a set of lightweight modifications that apply spectral normalization and replace the final dense layer with a Gaussian process layer to improve single-model uncertainty estimation and OOD detection. We evaluate SNGP vs. deterministic and MonteCarlo dropout on six datasets across three biomedical classification tasks: white blood cells, amyloid plaques, and colorectal histopathology. SNGP has comparable in-distribution performance while significantly improving uncertainty estimation and OOD detection. Thus, SNGP or related models offer a useful framework for uncertainty-aware classification in digital pathology, supporting safe deployment and building trust with pathologists.
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