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.

翻译：准确的病理学解读是临床决策的关键；然而，当前用于数字病理学的深度学习模型在分布外（OOD）场景中常常过度自信且校准性差，这限制了信任度和临床采用率。安全至上的医学影像工作流程受益于能够准确拒识OOD输入的内在不确定性感知特性。我们实现了谱归一化神经高斯过程（SNGP），这是一组轻量级修改，通过应用谱归一化并将最终的全连接层替换为高斯过程层，以改进单模型不确定性估计和OOD检测。我们在涵盖三种生物医学分类任务（白细胞、淀粉样斑块和结直肠组织病理学）的六个数据集上，评估了SNGP与确定性模型及蒙特卡洛Dropout方法的性能。SNGP在保持相当分布内性能的同时，显著提升了不确定性估计和OOD检测能力。因此，SNGP或相关模型为数字病理学中的不确定性感知分类提供了一个实用框架，有助于安全部署并建立与病理学家的信任。