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Fully automated classification of mammograms using deep residual neural networks.

, , and . ISBI, page 310-314. IEEE, (2017)

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Investigation of the non-Markovity Spectrum as a Cognitive Processing Measure of Deep Brain Microelectrode Recordings., , , , , , , , and . BIOSIGNALS, page 144-150. SciTePress, (2011)Rule Extraction from Support Vector Machines: A Sequential Covering Approach., and . IEEE Trans. Knowl. Data Eng., 19 (6): 729-741 (2007)Enhancing the classification accuracy of Steady-State Visual Evoked Potential-based Brain-Computer Interface using Component Synchrony Measure., , and . IJCNN, page 1-6. IEEE, (2012)Estimation Of Epipolar Geometry Via The Radon Transform., , and . ICASSP (2), page 497-500. IEEE, (2006)A deep learning approach for the analysis of masses in mammograms with minimal user intervention., , and . Medical Image Anal., (2017)Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells., , , , , , , and . IEEE J. Biomed. Health Informatics, 21 (2): 441-450 (2017)Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms., , and . ISBI, page 760-763. IEEE, (2015)Smocam: Smooth Conditional Attention Mask For 3d-Regression Models., , , , , , , , and . ISBI, page 362-366. IEEE, (2021)When to Use Augmentation - Variability Insufficient for Cortical Thickness Estimation Improvement., , , , , , , and . ISBI, page 1-5. IEEE, (2023)Model Agnostic Saliency For Weakly Supervised Lesion Detection From Breast DCE-MRI., , , , and . ISBI, page 1057-1060. IEEE, (2019)