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Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT., , , , and . Medical Image Anal., 18 (5): 725-739 (2014)Convolutional Invasion and Expansion Networks for Tumor Growth Prediction., , , , and . CoRR, (2018)Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning., , , , , , , , and . CoRR, (2016)Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging., , , , , , , , and . Deep Learning and Convolutional Neural Networks for Medical Image Computing, Springer, (2017)Reducing the False Positive Rate of Computer Aided Detection for CT Colonography Using Content Based Image Retrieval., , and . ISBI, page 915-918. IEEE, (2009)Supervised Augmentation: Leverage Strong Annotation for Limited Data., , , , , and . ISBI, page 1134-1138. IEEE, (2020)Computer Aided Monitoring of Fibrous Dysplasia Disease in Craniofacial Bones., , , and . ISBI, page 245-248. IEEE, (2004)Computer Aided Detection of Lytic Bone Metastases in the Spine Using Routine CT Images., , and . ISBI, page 512-515. IEEE, (2007)Tumor growth modeling based on dual phase CT and FDG-PET., , , , , and . ISBI, page 394-397. IEEE, (2013)Augmenting tumor sensitive matching flow to improve detection and segmentation of ovarian cancer metastases within a PDE framework., , , , and . ISBI, page 652-655. IEEE, (2013)