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Open AccessArticle

Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples

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Department of Mathematics, Beijing University of Chemical Technology, Beijing 100029, China
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State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083, China
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Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong 999077, China
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College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4401; https://doi.org/10.3390/s19204401
Received: 12 August 2019 / Revised: 30 September 2019 / Accepted: 9 October 2019 / Published: 11 October 2019
(This article belongs to the Special Issue Biomedical Imaging and Sensing)
Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. Such a design gives MSMKU a rich receptive field and is able to effectively represent multi-scale features. In addition, we designed a mixed maximum loss minimization learning strategy (MMLM) for training the proposed MSMKU. This training strategy can adaptively sort the samples by the loss function and re-weight the samples through data enhancement, thereby synchronously improving the prediction performance of all samples. Experiments show that the proposed method has obtained a state-of-the-art breakthrough result for OD and OC segmentation on the RIM-ONE-V3 and DRISHTI-GS datasets. At the same time, the proposed method achieved satisfactory glaucoma screening performance on the RIM-ONE-V3 and DRISHTI-GS datasets. On datasets with an imbalanced distribution between typical and rare sample images, the proposed method obtained a higher accuracy than existing deep learning methods. View Full-Text
Keywords: convolutional neural network; mixed maximum loss minimization; optic disc segmentation; optic cup segmentation; glaucoma screening convolutional neural network; mixed maximum loss minimization; optic disc segmentation; optic cup segmentation; glaucoma screening
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Xu, Y.-L.; Lu, S.; Li, H.-X.; Li, R.-R. Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples. Sensors 2019, 19, 4401.

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