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Weakly Supervised and Semi-Supervised Semantic Segmentation for Optic Disc of Fundus Image

by Zheng Lu and Dali Chen *
College of Information Science and Engineering, Heping District, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(1), 145; https://doi.org/10.3390/sym12010145 (registering DOI)
Received: 16 December 2019 / Revised: 3 January 2020 / Accepted: 8 January 2020 / Published: 10 January 2020
Weakly supervised and semi-supervised semantic segmentation has been widely used in the field of computer vision. Since it does not require groundtruth or it only needs a small number of groundtruths for training. Recently, some works use pseudo groundtruths which are generated by a classified network to train the model, however, this method is not suitable for medical image segmentation. To tackle this challenging problem, we use the GrabCut method to generate the pseudo groundtruths in this paper, and then we train the network based on a modified U-net model with the generated pseudo groundtruths, finally we utilize a small amount of groundtruths to fine tune the model. Extensive experiments on the challenging RIM-ONE and DRISHTI-GS benchmarks strongly demonstrate the effectiveness of our algorithm. We obtain state-of-art results on RIM-ONE and DRISHTI-GS databases. View Full-Text
Keywords: weakly supervised learning; semi-supervised learning; optic disc segmentation; deep learning; medical image processing weakly supervised learning; semi-supervised learning; optic disc segmentation; deep learning; medical image processing
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Lu, Z.; Chen, D. Weakly Supervised and Semi-Supervised Semantic Segmentation for Optic Disc of Fundus Image. Symmetry 2020, 12, 145.

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