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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)

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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)A deep learning approach for the analysis of masses in mammograms with minimal user intervention., , and . Medical Image Anal., (2017)Producing radiologist-quality reports for interpretable artificial intelligence., , , , and . CoRR, (2018)Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells., , , , , , , and . IEEE J. Biomed. Health Informatics, 21 (2): 441-450 (2017)Special issue: 4th MICCAI workshop on deep learning in medical image analysis., , , , , , and . Comput. methods Biomech. Biomed. Eng. Imaging Vis., 8 (5): 501 (2020)An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells., , and . IEEE Trans. Image Process., 24 (4): 1261-1272 (2015)Model Agnostic Saliency For Weakly Supervised Lesion Detection From Breast DCE-MRI., , , , and . ISBI, page 1057-1060. IEEE, (2019)When to Use Augmentation - Variability Insufficient for Cortical Thickness Estimation Improvement., , , , , , , and . ISBI, page 1-5. IEEE, (2023)Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms., , and . ISBI, page 760-763. IEEE, (2015)