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Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network., , , and . CoRR, (2018)MRI to X-ray mammography intensity-based registration with simultaneous optimisation of pose and biomechanical transformation parameters., , , , , , , , , and 2 other author(s). Medical Image Anal., 18 (4): 674-683 (2014)Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation., , and . CoRR, (2023)Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 109500J. SPIE, (2019)Automated lesion detection and segmentation in digital mammography using a u-net deep learning network., , , , and . IWBI, volume 10718 of SPIE Proceedings, page 1071805. SPIE, (2018)Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 1095002. SPIE, (2019)Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI., , , , , , , and . Medical Image Anal., (February 2024)Generating Synthetic Mammograms From Reconstructed Tomosynthesis Volumes., , , and . IEEE Trans. Med. Imaging, 32 (12): 2322-2331 (2013)Automated Characterization of Breast Lesions Imaged With an Ultrafast DCE-MR Protocol., , , , and . IEEE Trans. Med. Imaging, 33 (2): 225-232 (2014)A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography., , , , , , , and . Digital Mammography / IWDM, volume 9699 of Lecture Notes in Computer Science, page 51-56. Springer, (2016)