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- W3201682283 abstract "We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentangle intensity and non-intensity features within a unique two-stream auto-encoding architecture, and selectively adapt the non-intensity features that are believed to be more transferable across domains. The segmentation prediction is performed by aggregating the disentangled features. We evaluated our method using intravenous contrast-enhanced abdominal CT scans with and without oral contrast, which are used as source and target domains, respectively. The proposed method showed clear improvements in terms of three different metrics compared to other domain adaptation methods that are without the feature disentanglement. The method brings small bowel segmentation closer to clinical application." @default.
- W3201682283 created "2021-10-11" @default.
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- W3201682283 date "2021-01-01" @default.
- W3201682283 modified "2023-10-16" @default.
- W3201682283 title "Unsupervised Domain Adaptation for Small Bowel Segmentation Using Disentangled Representation" @default.
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- W3201682283 doi "https://doi.org/10.1007/978-3-030-87199-4_27" @default.
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