Biology subjects

Dugger, B. N.

Publications and source records attributed to Dugger, B. N..

7 recordsLinked to original sources

bioRxiv2026

Evaluating the Transferability of Pathology Foundation Models Across Cancer-related H&E Neurodegeneration-related Immunohistochemical Classification Tasks

Foundation models (FMs) have rapidly become dominant in artificial intelligence and are increasingly being adopted in computational pathology. Numerous pathology-specific FMs have been developed and evaluated for a variety of downstream tasks, most commonly using frozen image embeddings with linear probes. While a pathology FM, at least implicitly suggests broad reusability across tasks, the transferability of these models to neurodegenerative disease-related tasks remains largely unexplored. In this study, we evaluated fourteen frozen feature extractors, including general-vision and pathology FMs, across four pathology image classification datasets spanning two specialized neurodegenerative disease immuno-histochemistry (IHC) tasks (Tau neurofibrillary tangle (NFT) and amyloid-{beta} plaque classification) and two cancer-related hematoxylin and eosin (H&E) tasks (the breast tissue BACH dataset and the multi-tissue TIL dataset). The cancer-related H&E datasets represent tasks more closely aligned with the predominant pretraining domain of many current pathology FMs, whereas the neurodegenerative IHC datasets represent a more specialized domain with limited representation in existing FM pretraining cohorts. Frozen embedding linear probes were compared against a conventional supervised ResNet-50 convolutional neural network (CNN) trained directly on image tiles using identical train, validation, and test splits. Across both neurodegenerative IHC datasets, the supervised CNN substantially outperformed all frozen FM linear probes. In contrast, pathology FMs achieved performance comparable to, and in some cases exceeding, the supervised CNN across the two cancer-related H&E datasets, with UNI2-h achieving the highest performance on BACH and several pathology FMs performing on par with the CNN on the larger TIL dataset. Furthermore, a supervised CNN trained using only 1% of the Tau NFT training data (1,938 tiles) still exceeded the performance of the best frozen FM linear probe trained on the complete dataset. Together, these results suggest the transferability of frozen pathology FMs may depend strongly on how well the downstream task is represented by their pretraining domain. These findings demonstrate frozen pathology FMs transfer effectively to the cancer-related H&E tasks evaluated here but may be less effective for specialized neurodegenerative IHC tasks less well represented in current FM pretraining cohorts. Expanding pathology FM pretraining datasets to include a broader range of disease domains and staining modalities may therefore improve transferability to specialized pathology applications. Our findings also highlight the continued importance of conventional supervised learning and motivate future work investigating nonlinear probes and end-to-end FM fine-tuning.

pathology↗

bioRxiv2025

Spatial and spectral mapping of traffic-related air pollution (TRAP) nanoparticles in relation to plaques and inflammatory markers in an Alzheimer disease model

Chronic exposure to traffic-related air pollution (TRAP) is linked to increased risk of neurodegenerative diseases, including Alzheimer disease (AD). Ultrafine particulate matter (UFPM) is a suspected driver of TRAP neurotoxicity, but its spatial interactions with AD pathology remain poorly defined. We investigated the distribution, composition, and pathological context of TRAP-derived UFPM in the hippocampus of TgF344-AD rats chronically exposed to TRAP or filtered air (FA) for 14 months. Using a multimodal imaging workflow that combines enhanced darkfield hyperspectral imaging (EDF-HSI) with confocal immunofluorescence for microglia (CD68/Iba1) and amyloid beta (A{beta}) plaques (Thioflavin S), we mapped the localization and spectral properties of UFPM in situ. UFPM accumulation was elevated in TRAP-exposed females, suggesting sex-specific vulnerability in blood-brain barrier (BBB) permeability or particle retention. Particles near plaques showed red-shifted spectral signatures, consistent with biochemical transformation. Dimension reduction revealed clustering of particle spectra by TRAP exposure and plaque proximity. However, UFPM was rarely found within plaques or microglia, implying indirect neuroimmune modulation. These findings highlight a novel spatial and spectral imaging approach for characterizing environmental nanoparticle interactions in the brain and suggest that chronic TRAP exposure may influence AD-related inflammation and pathology in a sex-and region-dependent manner. SynopsisThis study shows that chronic traffic-related air pollution alters ultrafine particulate matter deposition and neuroinflammation in a rodent Alzheimer model, revealing region- and sex-specific vulnerability in the brains response to environmental exposure.

pharmacology and toxicology↗

bioRxiv2024

Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations

Accumulation of abnormal tau protein into neurofibrillary tangles (NFTs) is a pathologic hallmark of Alzheimer disease (AD). Accurate detection of NFTs in tissue samples can reveal relationships with clinical, demographic, and genetic features through deep phenotyping. However, expert manual analysis is time-consuming, subject to observer variability, and cannot handle the data amounts generated by modern imaging. We present a scalable, open-source, deep-learning approach to quantify NFT burden in digital whole slide images (WSIs) of post-mortem human brain tissue. To achieve this, we developed a method to generate detailed NFT boundaries directly from single-point-per-NFT annotations. We then trained a semantic segmentation model on 45 annotated 2400{micro}m by 1200{micro}m regions of interest (ROIs) selected from 15 unique temporal cortex WSIs of AD cases from three institutions (University of California (UC)-Davis, UC-San Diego, and Columbia University). Segmenting NFTs at the single-pixel level, the model achieved an area under the receiver operating characteristic of 0.832 and an F1 of 0.527 (196-fold over random) on a held-out test set of 664 NFTs from 20 ROIs (7 WSIs). We compared this to deep object detection, which achieved comparable but coarser-grained performance that was 60% faster. The segmentation and object detection models correlated well with expert semi-quantitative scores at the whole-slide level (Spearmans rho {rho}=0.654 (p=6.50e-5) and {rho}=0.513 (p=3.18e-3), respectively). We openly release this multi-institution deep-learning pipeline to provide detailed NFT spatial distribution and morphology analysis capability at a scale otherwise infeasible by manual assessment.

pathology↗

bioRxiv2023

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels

Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathies (CAAs). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes. We evaluated the detector on a new manually-annotated dataset of whole slide images (WSIs) from three institutions, four staining procedures, and four human experts. The detector matched the cohort of neuropathology experts, achieving 0.64 (model) vs. 0.64 (cohort) average precision (AP) for cored plaques and 0.75 vs. 0.51 AP for CAAs at a 0.5 IOU threshold. It provided count and locality predictions that correlated with gold-standard CERAD-like WSI scoring (p=0.07{+/-} 0.10). The openly-available model can quickly score WSIs in minutes without a GPU on a standard workstation.

pathology↗

bioRxiv2021

Deep learning from multiple experts improves identification of amyloid neuropathologies

Pathologists can label pathologies differently, making it challenging to yield consistent assessments in the absence of one ground truth. To address this problem, we present a DL approach that draws on a cohort of experts, weighs each contribution, and is robust to noisy labels. We collected 100,495 annotations on 20,099 candidate amyloid beta neuropathologies (cerebral amyloid angiopathy (CAA), and cored and diffuse plaques) from three institutions, independently annotated by five experts. DL methods trained on a consensus-of-two strategy yielded 12.6-26% improvements by area under the precision recall curve (AUPRC) when compared to those that learned individualized annotations. This strategy surpassed individual-expert models, even when unfairly assessed on benchmarks favoring them. Moreover, ensembling over individual models was robust to hidden random annotators. In blind prospective tests of 52,555 subsequent expert-annotated images, the models labeled pathologies like their human counterparts (consensus model AUPRC=0.74 cored; 0.69 CAA). This study demonstrates a means to combine multiple ground truths into a common-ground DL model that yields consistent diagnoses informed by multiple and potentially variable expert opinion.

pathology↗

bioRxiv2020

Interpretable Segmentation of Amyloid-βStained Whole Slide Images of Brain Tissue

Neurodegenerative disease pathologies have been reported in both grey matter (GM) and white matter (WM) with different density distributions, an automated separation of GM/WM would be extremely advantageous for aiding in neuropathologic deep phenotyping. Standard segmentation methods typically involve manual annotations, where a trained researcher traces the delineation of GM/WM in ultra-high-resolution Whole Slide Images (WSIs). This method can be time-consuming and subjective, preventing the analysis of large amounts of WSIs at scale. This paper proposes an automated segmentation pipeline combining a Convolutional Neural Network (CNN) module for segmenting GM/WM regions and a post-processing module to remove artifacts/residues of tissues as well as generate XML annotations that can be visualized via Aperio ImageScope. First, we investigate two baseline models for medical image segmentation: FCN, and U-Net. Then we propose a patch-based approach, ResNet-Patch, to classify the GM/WM/background regions. In addition, we integrate a Neural Conditional Random Field (NCRF) module, ResNet-NCRF, to model and incorporate the spatial correlations among neighboring patches. Although their mechanisms are greatly different, both U-Net and ResNet-Patch/ResNet-NCRF achieve Intersection over Union (IoU) of more than 90% in GM and more than 80% in WM, while ResNet-Patch achieves 1% superior to U-Net with lower variance among various WSIs. ResNet-NCRF further improves the IoU by 3% for WM compared to ResNet-Patch before post-processing. We also apply gradient-weighted class activation mapping (Grad-CAM) to interpret the segmentation masks and provide relevant explanations and insights.

neuroscience↗

bioRxiv2018

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and can lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline identifying specific neuropathologies--amyloid plaques and cerebral amyloid angiopathy--in immunohistochemical-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotated >70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieved strong plaque classification (0.993 and 0.744 areas under the receiver operating characteristic and precision recall curve, respectively) on a 10 WSI hold-out set. Prediction confidence maps visualized morphology distributions from the full-WSI level down to 20x magnification. Resulting plaque-burden scores correlated well with established semi-quantitative scores. Finally, saliency mapping demonstrated that networks learned patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologists ability may suggest a route to neuropathologic deep phenotyping.

pathology↗