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Edgeworth-based approximation of Mutual Information for medical image registration., , , and . IPTA, page 195-200. IEEE, (2010)Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images*., , , , , , , , , and 1 other author(s). EMBC, page 1536-1539. IEEE, (2020)Automatic classification of benign and malignant kidney masses using radiomics. A retrospective study exploiting the KiTS19 dataset., , , , and . Medical Imaging: Image Processing, volume 11596 of SPIE Proceedings, SPIE, (2021)Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning., , , , , , , , , and 43 other author(s). IEEE Trans. Medical Imaging, 42 (3): 697-712 (March 2023)Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies., , , , , , , , , and 2 other author(s). EMBC, page 4736-4739. IEEE, (2022)Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images., , , , , , , , , and 1 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 11597 of SPIE Proceedings, SPIE, (2021)Using Elastix to Register Inhale/Exhale Intrasubject Thorax CT: A Unsupervised Baseline to the Task 2 of the Learn2Reg Challenge., , and . MICCAI (Challenges), volume 12587 of Lecture Notes in Computer Science, page 100-105. Springer, (2020)Demons versus level-set motion registration for coronary 18F-sodium fluoride PET., , , , , , , , , and 1 other author(s). Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 97843Y. SPIE, (2016)Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning., , , , , , , , , and 37 other author(s). CoRR, (2021)Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer*., , , , , , , , , and 1 other author(s). EMBC, page 1532-1535. IEEE, (2020)