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- W3027204089 abstract "For the majority of the learning-based segmentation methods, a large quantity of high-quality training data is required. In this paper, we present a novel learning-based segmentation model that could be trained semi- or un- supervised. Specifically, in the unsupervised setting, we parameterize the Active contour without edges (ACWE) framework via a convolutional neural network (ConvNet), and optimize the parameters of the ConvNet using a self-supervised method. In another setting (semi-supervised), the auxiliary segmentation ground truth is used during training. We show that the method provides fast and high-quality bone segmentation in the context of single-photon emission computed tomography (SPECT) image." @default.
- W3027204089 created "2020-05-29" @default.
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- W3027204089 date "2020-01-25" @default.
- W3027204089 modified "2023-09-26" @default.
- W3027204089 title "Medical Image Segmentation via Unsupervised Convolutional Neural Network" @default.
- W3027204089 hasPublicationYear "2020" @default.
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