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- W3164270446 abstract "As a non-invasive imaging modality, Optical coherence tomography (OCT) has been widely used in clinical applications, mainly for monitoring the development of ophthalmic diseases. OCT can provide high-resolution images to reveal changes in retinal tissues, such as the accumulation of fluid caused by macular edema. This paper proposed an uncertainty-aware semi-supervised framework for retinal fluid segmentation. This framework composed of a teacher network and a student network, and they share the same network architecture. Each network consists of an encoder and three decoders. The three decoders in these two networks can simultaneously predict the probability map, contour map and distance map. A fusion operation is performed on the three maps predicted by the student network to obtain the segmentation results. The method proposed has been verified effectiveness in the RTOUCH challenge dataset." @default.
- W3164270446 created "2021-06-07" @default.
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- W3164270446 date "2021-04-13" @default.
- W3164270446 modified "2023-09-25" @default.
- W3164270446 title "Uncertainty-Aware Semi-Supervised Framework for Automatic Segmentation of Macular Edema in Oct Images" @default.
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- W3164270446 doi "https://doi.org/10.1109/isbi48211.2021.9433840" @default.
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