.png){kind=icon}
Return
Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.media.2020.101695.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Glaucoma is the leading cause of irreversible blindness in the world. Structure and function assessments play an important role in diagnosing glaucoma. Nowadays, Optical Coherence Tomography (OCT) imaging gains increasing popularity in measuring the structural change of eyes. However, few automated methods have been developed based on OCT images to screen glaucoma. In this paper, we are the first to unify the structure analysis and function regression to distinguish glaucoma patients from normal controls effectively. Specifically, our method works in two steps: a semi-supervised learning strategy with smoothness assumption is first applied for the surrogate assignment of missing function regression labels. Subsequently, the proposed multi-task learning network is capable of exploring the structure and function relationship between the OCT image and visual field measurement simultaneously, which contributes to classification performance improvement. It is also worth noting that the proposed method is assessed by two large-scale multi-center datasets. In other words, we first build the largest glaucoma OCT image dataset (i.e., HK dataset) involving 975,400 B-scans from 4,877 volumes to develop and evaluate the proposed method, then the model without further fine-tuning is directly applied on another independent dataset (i.e., Stanford dataset) containing 246,200 B-scans from 1,231 volumes. Extensive experiments are conducted to assess the contribution of each component within our framework. The proposed method outperforms the baseline methods and two glaucoma experts by a large margin, achieving volume-level Area Under ROC Curve (AUC) of 0.977 on HK dataset and 0.933 on Stanford dataset, respectively. The experimental results indicate the great potential of the proposed approach for the automated diagnosis system. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Optical coherence tomography
Deep learning
Glaucoma screening
Semi-supervised multi-task learning
Journal
IF:
11.8
Papers:
3.9K
Citations:
2.4W
Organization
Cited Papers
ShareSaveClinically applicable deep learning for diagnosis and referral in retinal disease.png)
NATURE MEDICINE
NATURE MEDICINEIF50
PREAI
ShareSaveShareSaveShareSaveFrom Machine to Machine An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus PhotographsOPHTHALMOLOGY
OAAI
OPHTHALMOLOGYIF9.5
OAAIShareSave
View more