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Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions.

, , , , , , , and . IEEE J. Biomed. Health Informatics, 20 (6): 1585-1594 (2016)

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Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results., , , , and . SASHIMI@MICCAI, volume 10557 of Lecture Notes in Computer Science, page 49-57. Springer, (2017)Fully Convolutional Network for Liver Segmentation and Lesions Detection., , , , and . LABELS/DLMIA@MICCAI, volume 10008 of Lecture Notes in Computer Science, page 77-85. (2016)Synthetic data augmentation using GAN for improved liver lesion classification., , , , and . ISBI, page 289-293. IEEE, (2018)Modeling the Intra-class Variability for Liver Lesion Detection Using a Multi-class Patch-Based CNN., , , , , and . Patch-MI@MICCAI, volume 10530 of Lecture Notes in Computer Science, page 129-137. Springer, (2017)Automatic liver volume segmentation and fibrosis classification., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 1057506. SPIE, (2018)Sparsity-based liver metastases detection using learned dictionaries., , , and . ISBI, page 1195-1198. IEEE, (2016)Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations., , , , , and . Neurocomputing, (2018)Automatic detection and segmentation of liver metastatic lesions on serial CT examinations., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9035 of SPIE Proceedings, page 903519. SPIE, (2014)Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions., , , , , , , and . IEEE J. Biomed. Health Informatics, 20 (6): 1585-1594 (2016)Multi-phase liver lesions classification using relevant visual words based on mutual information., , , , and . ISBI, page 407-410. IEEE, (2015)