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- W2806398633 abstract "This paper proposes a novel method in order to obtain voxel-level segmentation for three fluid lesion types (IR-F/SRF/PED) in OCT images provided by the ReTOUCH challenge [1]. The method is based on a deep neural network consisting of encoding and de-coding blocks connected with skip-connections which was trained using a combined cost function comprising of cross-entropy, dice and adversarial loss terms. The segmentation results on a held-out validation set shows that the network architecture and the loss functions used has resulted in improved retinal fluid segmentation. Our method was ranked fourth in the ReTOUCH challenge." @default.
- W2806398633 created "2018-06-13" @default.
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- W2806398633 date "2018-04-01" @default.
- W2806398633 modified "2023-10-14" @default.
- W2806398633 title "Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks" @default.
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- W2806398633 doi "https://doi.org/10.1109/isbi.2018.8363842" @default.
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