Deep Learning–Based Histopathologic Assessment of Kidney Tissue | Zendy

Meyke Hermsen | Zendy; Thomas de Bel | Zendy; Marjolijn den Boer | Zendy; Eric J. Steenbergen | Zendy; Jesper Kers | Zendy; Sandrine Florquin | Zendy; Joris J. T. H. Roelofs | Zendy; Mark D. Stegall | Zendy; Mariam P. Alexander | Zendy; Byron H. Smith | Zendy; Bart Smeets | Zendy; Luuk B. Hilbrands | Zendy; Jeroen van der Laak | Zendy

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Deep Learning–Based Histopathologic Assessment of Kidney Tissue

Author(s) -

Meyke Hermsen,

Thomas de Bel,

Marjolijn den Boer,

Eric J. Steenbergen,

Jesper Kers,

Sandrine Florquin,

Joris J. T. H. Roelofs,

Mark D. Stegall,

Mariam P. Alexander,

Byron H. Smith,

Bart Smeets,

Luuk B. Hilbrands,

Jeroen van der Laak

Publication year - 2019

Publication title -

journal of the american society of nephrology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 4.451

H-Index - 279

eISSN - 1533-3450

pISSN - 1046-6673

DOI - 10.1681/asn.2019020144

Subject(s) - convolutional neural network , artificial intelligence , segmentation , computer science , kidney , pathology , histopathology , nephrectomy , pattern recognition (psychology) , artificial neural network , medicine , radiology

The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid-Schiff (PAS).

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