Pathological Myopic Image Analysis with Transfer Learning

2019/07/31 by Ruitao Xie, Libo Liu, Xie, Ruitao +5
Computer Science · Medicine · #Digital Imaging for Blood Diseases #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Retinal Imaging and Analysis #Retinal and Optic Conditions #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1908.00410

openalex publication_date 2019/07/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a summary of transfer learning based methods for several challenging myopic fundus image analysis tasks including classification of pathological and non-pathological myopia,localisation of fovea,and segmentation of optic disc.By adapting existing popular deep learning architectures,our proposed methods have achieved 1st and 2nd place in several tasks at the Pathologic Myopia Challenge held at ISBI2019.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Global Prevalence of Myopia and High Myopia and Temporal Trends from 2000 through 2050
• Transfusion: Understanding Transfer Learning for Medical Imaging

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