Unifying structure analysis and surrogate-driven function regression for glaucoma OCT image screening
Xi Wang,Hao Chen,Luyang Luo,Anran Ran,Poemen P. Chan,Clement C Y Tham,Carol Y. Cheung,Pheng-Ann Heng +7 more
- 13 Oct 2019
- pp 39-47
TL;DR: The proposed multi-task learning network is capable of exploring the structure and function relationship from the OCT image and visual field measurement simultaneously, which contributes to classification performance boosting and is the first to unify the structure analysis and function regression for glaucoma screening.
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Abstract: Optical Coherence Tomography (OCT) imaging plays an important role in glaucoma diagnosis in clinical practice. Early detection and timely treatment can prevent glaucoma patients from permanent vision loss. However, only a dearth of automated methods has been developed based on OCT images for glaucoma study. In this paper, we present a novel framework to effectively classify glaucoma OCT images from normal ones. A semi-supervised learning strategy with smoothness assumption is applied for surrogate assignment of missing function regression labels. Besides, the proposed multi-task learning network is capable of exploring the structure and function relationship from the OCT image and visual field measurement simultaneously, which contributes to classification performance boosting. Essentially, we are the first to unify the structure analysis and function regression for glaucoma screening. It is also worth noting that we build the largest glaucoma OCT image dataset involving 4877 volumes to develop and evaluate the proposed method. Extensive experiments demonstrate that our framework outperforms the baseline methods and two glaucoma experts by a large margin, achieving 93.2%, 93.2% and 97.8% on accuracy, F1 score and AUC, respectively.
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Citations
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TL;DR: In this paper , a cross-modal knowledge transfer method is designed by integrating a designed distillation loss and a proposed asynchronous feature regularization (AFR) module to transfer the complementary knowledge from the structural and functional assessments to the OCT model.
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