Retinal abnormalities recognition using regional multitask learning

Xin Wang, Lie Ju, Xin Zhao, Zongyuan Ge

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)

Abstract

The number of people suffering from retinal diseases increases with population aging and the popularity of electronic screens. Previous studies on deep learning based automatic screening generally focused on specific types of retinal diseases, such as diabetic retinopathy and glaucoma. Since patients may suffer from various types of retinal diseases simultaneously, these solutions are not clinically practical. To address this issue, we propose a novel deep learning based method that can recognise 36 different retinal diseases with a single model. More specifically, the proposed method uses a region-specific multi-task recognition model by learning diseases affecting different regions of the retina with three sub-networks. The three sub-networks are semantically trained to recognise diseases affecting optic-disc, macula and entire retina. Our contribution is two-fold. First, we use multitask learning for retinal disease classification and achieve significant improvements for recognising three main groups of retinal diseases in general, macular and optic-disc regions. Second, we collect a multi-label retinal dataset to the community as standard benchmark and release it for further research opportunities.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019
Subtitle of host publication22nd International Conference, Proceedings
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Place of PublicationCham Switzerland
PublisherSpringer
Pages30-38
Number of pages9
Edition1st
ISBN (Electronic)9783030322397
ISBN (Print)9783030322380
DOIs
Publication statusPublished - 2019
EventMedical Image Computing and Computer-Assisted Intervention 2019 - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019
Conference number: 22nd
https://www.miccai2019.org/

Publication series

NameLecture Notes in Computer Science
Volume11764
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceMedical Image Computing and Computer-Assisted Intervention 2019
Abbreviated titleMICCAI 2019
CountryChina
CityShenzhen
Period13/10/1917/10/19
Internet address

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