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Synthetic microbleeds generation for classifier training without ground truth.

, , , , , , and . Comput. Methods Programs Biomed., (2021)

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Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform., , , , , , and . ISBI, page 113-116. IEEE, (2014)High Contrast T1-Weigthed Mri with Fluid and White Matter Suppression Using Mp2Rage., , , , , , , , and . ISBI, page 701-704. IEEE, (2019)A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping., , , , , , , , , and 2 other author(s). ISBI, page 97-100. IEEE, (2017)Automated cartilage segmentation from 3D MR images of hip joint using an ensemble of neural networks., , , , , and . ISBI, page 1070-1073. IEEE, (2017)Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis., , , , , , , , , and 5 other author(s). CoRR, (2023)3D Brain MRI GAN-Based Synthesis Conditioned on Partial Volume Maps., , , , , , and . SASHIMI@MICCAI, volume 12417 of Lecture Notes in Computer Science, page 11-20. Springer, (2020)PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels., , , , , , and . ICCV, page 16673-16684. IEEE, (2023)Learning Camera Pose from Optical Colonoscopy Frames Through Deep Convolutional Neural Network (CNN)., , , , , and . CARE/CLIP@MICCAI, volume 10550 of Lecture Notes in Computer Science, page 50-59. Springer, (2017)Computational analysis of PET by AIBL (CapAIBL): a cloud-based processing pipeline for the quantification of PET images., , , , , and . Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 94132V. SPIE, (2015)Nonrigid correction of interleaving artefacts in pelvic MRI., , , , , , , , , and 2 other author(s). Medical Imaging: Image Processing, volume 7259 of SPIE Proceedings, page 72592P. SPIE, (2009)