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Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields.

, , , , , , , , , , , , and . MICCAI (2), volume 9901 of Lecture Notes in Computer Science, page 415-423. (2016)

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Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields., , , , , , , , , and 3 other author(s). MICCAI (2), volume 9901 of Lecture Notes in Computer Science, page 415-423. (2016)A large annotated medical image dataset for the development and evaluation of segmentation algorithms., , , , , , , , , and 14 other author(s). CoRR, (2019)The Liver Tumor Segmentation Benchmark (LiTS)., , , , , , , , , and 49 other author(s). CoRR, (2019)Diabetes60 - Inferring Bread Units From Food Images Using Fully Convolutional Neural Networks., , , , , , , , and . ICCV Workshops, page 1526-1535. IEEE Computer Society, (2017)Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks., , , , , , , , , and 10 other author(s). CoRR, (2017)Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation., , , , and . CoRR, (2017)SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D Convolutional Neural Networks., , , , , , , , , and . ISBI, page 839-843. IEEE, (2017)