Article

Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection

Authors:

Xiaogang WangAuthors Info & Claims

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III

Pages 267 - 275

https://doi.org/10.1007/978-3-319-66179-7_31

Published: 10 September 2017 Publication History

Abstract

We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions. Our contributions are two folds: (1) a network termed Zoom-in-Net which mimics the zoom-in process of a clinician to examine the retinal images. Trained with only image-level supervisions, Zoom-in-Net can generate attention maps which highlight suspicious regions, and predicts the disease level accurately based on both the whole image and its high resolution suspicious patches. (2) Only four bounding boxes generated from the automatically learned attention maps are enough to cover 80% of the lesions labeled by an experienced ophthalmologist, which shows good localization ability of the attention maps. By clustering features at high response locations on the attention maps, we discover meaningful clusters which contain potential lesions in diabetic retinopathy. Experiments show that our algorithm outperform the state-of-the-art methods on two datasets, EyePACS and Messidor.

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Cited By

Romero-Oraá RHerrero-Tudela MLópez MHornero RGarcía M(2024)Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy gradingComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108160249:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.cmpb.2024.108160
Bala RSharma AGoel N(2024)CTNet: convolutional transformer network for diabetic retinopathy classificationNeural Computing and Applications10.1007/s00521-023-09304-336:9(4787-4809)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00521-023-09304-3
Tahiri MAmakdouf HEl mallahi MQjidaa H(2023)Optimized quaternion radial Hahn Moments application to deep learning for the classification of diabetic retinopathyMultimedia Tools and Applications10.1007/s11042-023-15582-982:30(46217-46240)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11042-023-15582-9
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cover image Guide Proceedings

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III

Sep 2017

696 pages

ISBN:978-3-319-66178-0

DOI:10.1007/978-3-319-66179-7

Editors:
Maxime Descoteaux
Université de Sherbrooke, Sherbrooke, QC, Canada
,
Lena Maier-Hein
DKFZ, Heidelberg, Germany
,
Alfred Franz
Ulm University of Applied Sciences, Ulm, Germany
,
Pierre Jannin
Université de Rennes 1, Rennes, France
,
D. Louis Collins
McGill University, Montreal, QC, Canada
,
Simon Duchesne
Université Laval, Québec, QC, Canada

© Springer International Publishing AG 2017.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 10 September 2017

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Cited By

Romero-Oraá RHerrero-Tudela MLópez MHornero RGarcía M(2024)Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy gradingComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108160249:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.cmpb.2024.108160
Bala RSharma AGoel N(2024)CTNet: convolutional transformer network for diabetic retinopathy classificationNeural Computing and Applications10.1007/s00521-023-09304-336:9(4787-4809)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00521-023-09304-3
Tahiri MAmakdouf HEl mallahi MQjidaa H(2023)Optimized quaternion radial Hahn Moments application to deep learning for the classification of diabetic retinopathyMultimedia Tools and Applications10.1007/s11042-023-15582-982:30(46217-46240)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11042-023-15582-9
Li HZhu WChen XWang Y(2023)Prediction of Spherical Equivalent with Vanilla ResNetMyopic Maculopathy Analysis10.1007/978-3-031-54857-4_6(66-74)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-54857-4_6
Zhu WQiu PChen XLi HWang HLepore NDumitrascu OWang Y(2023)Beyond MobileNet: An Improved MobileNet for Retinal DiseasesMyopic Maculopathy Analysis10.1007/978-3-031-54857-4_5(56-65)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-54857-4_5
Cheng SHou QCao PYang JLiu XZaiane O(2023)Lesion-Aware Contrastive Learning for Diabetic Retinopathy DiagnosisMedical Image Computing and Computer Assisted Intervention – MICCAI 202310.1007/978-3-031-43990-2_63(671-681)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-43990-2_63
Li RGu YWang XLu S(2022)Bag of Tricks for Diabetic Retinopathy Grading of Ultra-Wide Optical Coherence Tomography Angiography ImagesMitosis Domain Generalization and Diabetic Retinopathy Analysis10.1007/978-3-031-33658-4_3(26-30)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-33658-4_3
Yan XLi ZWen JPan L(2022)A Transfer Learning Based Model Ensemble Method for Image Quality Assessment and Diabetic Retinopathy GradingMitosis Domain Generalization and Diabetic Retinopathy Analysis10.1007/978-3-031-33658-4_17(178-185)Online publication date: 18-Sep-2022
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Che HJin HChen H(2022)Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature DisentanglementMedical Image Computing and Computer Assisted Intervention – MICCAI 202210.1007/978-3-031-16437-8_50(523-533)Online publication date: 18-Sep-2022
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Zhou YHe XCui SZhu FLiu LShao L(2019)High-Resolution Diabetic Retinopathy Image Synthesis Manipulated by Grading and LesionsMedical Image Computing and Computer Assisted Intervention – MICCAI 201910.1007/978-3-030-32239-7_56(505-513)Online publication date: 13-Oct-2019
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