Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels

Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels

Purpose: To compare the efficacy and efficiency of training neural networks for medical image classification using comparison labels indicating relative disease severity versus diagnostic class labels from a retinopathy of prematurity (ROP) image dataset. Design: Evaluation of diagnostic test or tec...

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Bibliographic Details
Main Authors: Adam Hanif, MD, İlkay Yıldız, PhD, Peng Tian, PhD, Beyza Kalkanlı, BS, Deniz Erdoğmuş, PhD, Stratis Ioannidis, PhD, Jennifer Dy, PhD, Jayashree Kalpathy-Cramer, PhD, Susan Ostmo, MS, Karyn Jonas, BSN, R. V. Paul Chan, MD, MBA, Michael F. Chiang, MD, J. Peter Campbell, MD, MPH
Format: Article
Language:English
Published: Elsevier 2022-06-01
Series:Ophthalmology Science
Subjects:
Artificial intelligence
Deep learning
Labels
Neural networks
Retinopathy of prematurity
Online Access:http://www.sciencedirect.com/science/article/pii/S2666914522000112

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