Generalizing Amyloid β Pathology from Postmortem Human Brains with Vision Foundation Models for Lesion Quantification

Traditional neuropathological assessment of Alzheimer's disease (AD) relies on manual, semi-quantitative scoring of amyloid-beta (Aβ) burden. This approach is limited by high inter-rater variability and often qualitative, failing to capture subtle, continuous pathology changes. While 11C-PiB PET neuroimaging provides non-invasive longitudinal monitoring, it has a limited dynamic range and lacks the sub-millimeter spatial resolution necessary to track localized microvascular and parenchymal Aβ trajectories.

We introduce ViTamyloid, a generalizable modular computational pathology pipeline for high-throughput Aβ segmentation and classification in postmortem tissue. In Stage 1, a pathologist-supervised hybrid framework combining an EfficientNet-B4 encoder with a Residual U-Net decoder, complement with a localization-aware Faster R-CNN detector, to generate binary segmentation masks and isolated lesion candidates. In Stage 2, classification model powered by a frozen, high-resolution UNI2-h computational pathology foundation backbone and a ViT-Adapter, classifies candidates into a fine-grained four-class taxonomy: classic cored plaques (CCP), coarse-grained plaques (CGP), capillary cerebral amyloid angiopathy (CAA Type 1), and parenchymal/leptomeningeal CAA (CAA Type 2). We bench-marked out-of-distribution (OOD) robustness on external datasets from different brain banks, and validated clinical scalability across 1,646 whole-slide images from three brain banks.

The ViTamyloid classifier achieved strong feature-separation metrics, yielding area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) values of 0.994/0.958 for CCP, 0.989/0.945 for CGP, 0.992/0.977 for CAA Type 1, and 0.997/0.978 for CAA Type 2. More importantly, ViTamyloid consistently outperforms other pipelines' models and frozen features in all external datasets with different antibodies, and scanner settings, and image quality.

By replacing empirical, from-scratch architectures with a generalizable foundation model adapter framework, ViTamyloid provides a highly sensitive scalable digital pathomics toolkit. This pipeline expose continuous microscopic neuropathological dynamics beyond the spatial and sensitivity plateaus of in vivo neuroimaging, offering objective, quantitative endpoints for Alzheimer's disease clinical trials and disease subtyping.