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

Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations

A scalable, open-source, deep-learning approach to quantify NFT burden in digital whole slide images (WSIs) of post-mortem human brain tissue and openly releases this multi-institution deep-learning pipeline to provide detailed NFT spatial distribution and morphology analysis capability at a scale otherwise infeasible...

A validated deep learning workflow for 3D quantification of amyloid plaques in cleared mouse brain.

A validated and scalable 3D analysis pipeline for whole-brain quantification of amyloid plaques in cleared mouse brain tissue imaged by light-sheet microscopy is presented, providing a platform for neuropathological phenotyping and therapeutic evaluation in preclinical AD research.

Alzheimer’s disease detection from structural brain MRI using FFA U-Net segmentation and transfer learning

The findings suggest that combining attention-augmented segmentation with transfer learning-based classification can effectively support automated AD detection from structural MRI, potentially reducing the manual burden on clinicians.

A hybrid CNN–BiLSTM network with additive attention for automated four-stage classification of Alzheimer's disease from brain MRI

AD is a progressive and irreversible neurodegenerative disorder. Because the structural changes that accompany the earliest stages of the disease are subtle and easily overlooked, manual interpretation of MRI is time-consuming and prone to inter-observer disagreement. This study presents an end-to-end deep learning fra...

Examining the Clinical Utility of Deep Learning Models for Perivascular Space Segmentation in Alzheimer's Disease

CANDI: A Hybrid Deep Cortical Atrophy Identifier for High-Precision Alzheimer’s Disease Prediction using MRI