Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Im; Second International Carole H. Sudre,

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Mustafa Sa?lam,Islem Rekikding of electrodennal activity is one of the most frequently used methods in psychophysiology. Indeed, in the early years following the founding of the Society for Psychophysiological Research, electrodennal research so dominated the field that people worried that the society was simply an electrodennal socie978-1-4613-6241-8978-1-4615-2864-7

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Karthik Gopinath,Christian Desrosiers,Herve Lombaert articles are generally invited by the volume editors. All chapters from Topics in Organometallic Chemistry are published OnlineFirst with an individual DOI. In references, Topics in Organometallic Chemistry is abbreviated as Top Organomet Chem and cited as a journal.978-3-319-81505-3978-3-319-33414-1Series ISSN 1436-6002 Series E-ISSN 1616-8534

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0302-9743 ng together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts..978-3-030-60364-9978-3-030-60365-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Image Registration via Stochastic Gradient Markov Chain Monte Carloerior distribution. Regarding the modelling issues, we carefully design a Bayesian model for registration to overcome the existing barriers when using a dense, high-dimensional, and diffeomorphic parameterisation of the transformation. This results in improved calibration of uncertainty estimates.

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Image Registration via Stochastic Gradient Markov Chain Monte Carloabilistic registration of large images along with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the . and the . frameworks in order to efficiently draw thousands of samples from the post

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