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Magnetic Resonance Imaging|January 6, 2024
On retrospective k-space subsampling schemes for deep MRI reconstructionGeorge Yiasemis, Clara I Sánchez, Jan-Jakob Sonke, et al.Medical Physics|August 9, 2021
Automatic breast lesion detection in ultrafast DCE-MRI using deep learningFazael Ayatollahi, Shahriar B Shokouhi, Ritse M Mann, et al.Nature Communications|June 11, 2026
Towards robust foundation models for digital pathologyJonah Kömen, Edwin D de Jong, Julius Hense, et al.Medical Image Analysis|May 21, 2022
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancerYoni Schirris, Efstratios Gavves, Iris Nederlof, et al.Diagnostics (Basel, Switzerland)|July 27, 2022
Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRIRiccardo Samperna, Nikita Moriakov, Nico Karssemeijer, et al.Nature Communications|December 8, 2025
Mammo-AGE: deep learning estimation of breast age from mammogramsXin Wang, Tao Tan, Yuan Gao, et al.Magnetic Resonance in Medical Sciences : MRMS : an Official Journal of Japan Society of Magnetic Resonance in Medicine|June 15, 2025
Application of Machine Learning to Breast MR ImagingRoberto Lo Gullo, Vivien van Veldhuizen, Tina Roa, et al.Journal of Magnetic Resonance Imaging : JMRI|July 7, 2025
Potential Time and Recall Benefits for Adaptive AI-Based Breast Cancer MRI ScreeningLuuk Balkenende, Jonatan Ferm, Vivien van Veldhuizen, et al.Journal of Vision|September 3, 2020
Machine learning-based classification of viewing behavior using a wide range of statistical oculomotor featuresTimo Kootstra, Jonas Teuwen, Jeroen Goudsmit, et al.Medical Image Analysis|February 26, 2026
Incorporating global-local tissue changes to predict future breast cancer from longitudinal screening mammogramsXin Wang, Tao Tan, Yuan Gao, et al.Pageof 8