Image and label patches used to train GLASS-AI

This archive contains the paired image and label patches used to train our machine learning pipeline, Grading of Lung Adenocarcinoma with Simultaneous Segmentation by Artificial Intelligence (GLASS-AI). Image patches were generated from whole slide images of H&E-stained sections using an Aperio...

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Bibliographische Detailangaben
Hauptverfasser:	Lockhart, John H, Ackerman, Hayley D, Lee, Kyubum, Abdalah, Mahmoud, Davis, Andrew John, Hackel, Nicole, Boyle, Theresa A, Saller, James, Keske, Aysenur, Hänggi, Kay, Ruffell, Brian, Stringfield, Olya, Cress, W. Douglas, Tan, Aik Choon, Flores, Elsa R
Format:	Dataset
Sprache:	eng
Schlagworte:	digital pathology GLASS-AI H&E images lung adenocarcinoma - mouse machine learning training data
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Beschreibung
Zusammenfassung:	This archive contains the paired image and label patches used to train our machine learning pipeline, Grading of Lung Adenocarcinoma with Simultaneous Segmentation by Artificial Intelligence (GLASS-AI). Image patches were generated from whole slide images of H&E-stained sections using an Aperio ScanScope AT2 Slide Scanner (Leica) at 20x magnification with a 0.5022 microns/pixel resolution. The individual tumors and airways were annotated by an expert human before being divided into 224x224 pixel patches of the H&E image and paired annotation layer. For more details regarding how these data were used to train GLASS-AI, please see our forthcoming manuscript.
DOI:	10.5281/zenodo.7967748