Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks

Abstract

A Convolutional Neural Network was used to predict kidney function in patients with chronic kidney disease from high-resolution digital pathology scans of their kidney biopsies. Kidney biopsies were taken from participants of the NEPTUNE study, a longitudinal cohort study whose goal is to set up infrastructure for observing the evolution of 3 forms of idiopathic nephrotic syndrome, including developing predictors for progression of kidney disease. The knowledge of future kidney function is desirable as it can identify high-risk patients and influence treatment decisions, reducing the likelihood of irreversible kidney decline.

11 pages, 7 figures, 1 page of Appendix

References in corpus (3)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ADADELTA: An Adaptive Learning Rate Method

Cited by in corpus (1)

• A comment on the paper Prediction of Kidney Function from Biopsy Images using Convolutional Neural Networks