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Datasets for Publication "Artificial intelligence for tumor detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study" Tolkach Y. et al.

Authors: Tolkach, Yuri;

Datasets for Publication "Artificial intelligence for tumor detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study" Tolkach Y. et al.

Abstract

These are four test datasets for the publication "Artificial intelligence for tumor detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study" by Tolkach Y. et al. For dataset details, tissue classes, and number of images in each slide see original publication. All image patches were extracted using QuPath with a size 256 px at resolution MPP = 0.7813. All images are native scan quality without any normalization. For training of algorithms we reduced patches to 220 px (MPP = 0.922) corresponding to regions of 200 x 200 µm.

Related Organizations
Keywords

esophageal, carcinoma, digital pathology, tumor, detection

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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