Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.
DOI10.5281/zenodo.1470797Zenodo1470797MaRDI QIDQ6717763
Dataset published at Zenodo repository.
Charles Decarli, Ziqi Tang, Kangway Chuang, Lee-Way Jin, Laurel A. Beckett, Brittany Dugger, Michael Keiser
Publication date: 1 November 2018
Copyright license: Creative Commons Attribution 4.0 International
Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid- pathology expert annotations. Paper: Interpretable classification of Alzheimers disease pathologies with a convolutional neural network pipeline, bioRxiv454793;DOI:https://doi.org/10.1101/454793. Details:A total of 63 WSIs for 63 unique decedent cases spanning Alzheimers disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows: Development (Phases I-II). 33 WSIs used for convolutional neural network (CNN) model development(29 training, 4 validation). Hold-out (Phase III). 10 WSIs selected by an expert neuropathologistas a held-out test set to assess the generalizability of the CNN model. CERAD-like hold-out. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study. Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images),validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented. Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSVfiles. Slide source and preparation:All samples were retrieved from archives of the University of California, Davis Alzheimers Disease Center Brain Bank (https://www.ucdmc.ucdavis.edu/alzheimers/). Archival samples analyzed in this study were 5 m formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid- antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification. Code: Please visit https://github.com/keiserlab/plaquebox-paper
This page was built for dataset: Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.
