Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies

Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist’s workflow through screening and prioritizing slides with lesions and highlighting areas with specific lesions for review. Herein, we describe the comparis...

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Bibliographic Details
Published in:	Toxicologic pathology Vol. 49; no. 4; pp. 815 - 842
Main Authors:	Kuklyte, Jogile, Fitzgerald, Jenny, Nelissen, Sophie, Wei, Haolin, Whelan, Aoife, Power, Adam, Ahmad, Ajaz, Miarka, Martyna, Gregson, Mark, Maxwell, Michael, Raji, Ruka, Lenihan, Joseph, Finn-Moloney, Eve, Rafferty, Mairin, Cary, Maurice, Barale-Thomas, Erio, O’Shea, Donal
Format:	Journal Article
Language:	English
Published:	Los Angeles, CA SAGE Publications 01-06-2021
Subjects:	Original digital pathology convolutional neural network whole-slide imaging multi-magnification artificial intelligence generalized lesion detection data curation
Online Access:	Get full text
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Summary:	Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist’s workflow through screening and prioritizing slides with lesions and highlighting areas with specific lesions for review. Herein, we describe the comparison of various single- and multi-magnification convolutional neural network (CNN) architectures to accelerate the detection of lesions in tissues. Different models were evaluated for defining performance characteristics and efficiency in accurately identifying lesions in 5 key rat organs (liver, kidney, heart, lung, and brain). Cohorts for liver and kidney were collected from TG-GATEs open-source repository, and heart, lung, and brain from internally selected R&D studies. Annotations were performed, and models were trained on each of the available lesion classes in the available organs. Various class-consolidation approaches were evaluated from generalized lesion detection to individual lesion detections. The relationship between the amount of annotated lesions and the precision/accuracy of model performance is elucidated. The utility of multi-magnification CNN implementations in specific tissue subtypes is also demonstrated. The use of these CNN-based models offers users the ability to apply generalized lesion detection to whole-slide images, with the potential to generate novel quantitative data that would not be possible with conventional image analysis techniques.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0192-6233 1533-1601
DOI:	10.1177/0192623320986423