Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the fo...

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Bibliographic Details
Published in:	Cancers Vol. 13; no. 21; p. 5522
Main Authors:	Ruini, Cristel, Schlingmann, Sophia, Jonke, Žan, Avci, Pinar, Padrón-Laso, Víctor, Neumeier, Florian, Koveshazi, Istvan, Ikeliani, Ikenna U, Patzer, Kathrin, Kunrad, Elena, Kendziora, Benjamin, Sattler, Elke, French, Lars E, Hartmann, Daniela
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 03.11.2021 MDPI
Subjects:	Algorithms Artificial intelligence Big Data Cancer Confocal microscopy convolutional networks; digital pathology; digital staining; ex vivo confocal laser scanning microscopy; fluorescence confocal microscopy; machine learning; mohs surgery; neural networks; reflectance confocal microscopy; squamous cell carcinoma Decision making Deep learning Dermatology Histopathology Lasers Learning algorithms Machine learning Medical equipment Micrography Microscopy Neural networks Paraffin Pathology Segmentation Skin cancer Squamous cell carcinoma squamous cell carcinoma; ex vivo confocal laser scanning microscopy; reflectance confocal microscopy; fluorescence confocal microscopy; digital pathology; digital staining; neural networks; convolutional networks; machine learning; Mohs surgery Tumors United States > US Germany
ISSN:	2072-6694, 2072-6694
Online Access:	Get full text
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