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Cerebral Microbleeds Detection Using a 3D Feature Fused Region Proposal Network with Hard Sample Prototype Learning.

, , , , and . MICCAI (1), volume 13431 of Lecture Notes in Computer Science, page 452-460. Springer, (2022)

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A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI., , , , , and . Comput. Biol. Medicine, (February 2023)Toward Automated Detection of Microbleeds with Anatomical Scale Localization: A Complete Clinical Diagnosis Support Using Deep Learning., , , , , , , , and . CoRR, (2023)A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection., , , , and . EMBC, page 1055-1058. IEEE, (2020)Simultaneous Detection and Classification of Breast Masses in Digital Mammograms via a Deep Learning YOLO-based CAD System., , , , , , , , , and . Comput. Methods Programs Biomed., (2018)Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network., , , , , , , , and . EMBC, page 1230-1233. IEEE, (2017)Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI., , , , , , and . NeuroImage, (2022)A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification., , , , and . Int. J. Medical Informatics, (2018)Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification., , and . Comput. Methods Programs Biomed., (2020)A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net)., , , , and . AICAS, page 1-4. IEEE, (2022)Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI., , , , , , and . CoRR, (2021)