Article Information
PubMed
Published By
Print ISSN
Online ISSN
History
- Received for publication December 22, 2018
- Accepted for publication June 17, 2019
- Published online September 30, 2019.
Article Versions
- Earlier version (September 5, 2019 - 06:03).
- You are viewing the most recent version of this article.
Copyright & Usage
Copyright © 2019 by the American Society of Nephrology
Author Information
- Brandon Ginley1,
- Brendon Lutnick1,
- Kuang-Yu Jen2,
- Agnes B. Fogo3,
- Sanjay Jain4,
- Avi Rosenberg5,
- Vighnesh Walavalkar6,
- Gregory Wilding7,
- John E. Tomaszewski1,8,
- Rabi Yacoub9,
- Giovanni Maria Rossi5,10 and
- Pinaki Sarder1,7,11
- Departments of 1Pathology and Anatomical Sciences,
- 7Biostatistics,
- 8Biomedical Informatics, and
- 11Biomedical Engineering, and
- 9Division of Nephrology, Department of Medicine, University at Buffalo–The State University of New York, Buffalo, New York;
- 2Department of Pathology and Laboratory Medicine, University of California, Davis Medical Center, Sacramento, California;
- 3Departments of Pathology, Microbiology, and Immunology and Medicine, Vanderbilt University, Nashville, Tennessee;
- 4Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri;
- 5Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland;
- 6Department of Pathology, University of California San Francisco, San Francisco, California; and
- 10U.O. Nefrologia, Azienda Ospedaliero-Universitaria di Parma, Dipartimento di Medicina e Chirurgia, Università di Parma
- Correspondence:
Prof. Pinaki Sarder, University at Buffalo–The State University of New York, 955 Main Street, Rm 4204, Buffalo, NY 14203. Email: pinakisa{at}buffalo.edu
Funding
University at Buffalo
National Institute of Diabetes and Digestive and Kidney Diseases
DK076169NIDDK
R01 DK114485
Statistics from Altmetric.com
Article usage
Cited By...
In this issue
Computational Segmentation and Classification of Diabetic Glomerulosclerosis
Jump to section
More in this TOC Section
Cited By...
- PodoCount: A robust, fully automated whole-slide podocyte quantification tool
- Deep learning identifies pathological abnormalities predictive of graft loss in kidney transplant biopsies
- Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis
- Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension
- Deep learning driven quantification of interstitial fibrosis in kidney biopsies
- Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology
- Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach
- Artificial Intelligence: The Next Frontier in Kidney Biopsy Evaluation
- Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks
- Deep learning-based molecular morphometrics for kidney biopsies
- Imaging Renal Ultrastructure using a Fast and Simple Optical Clearing and Swelling Protocol
- Machine Learning Comes to Nephrology