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Abstract:
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In this talk, I would discuss the use of flexible Bayesian approaches in biomedical imaging. First, we will discuss applications to the analysis of task-related fMRI data in single-subject and multi-subject experiments, where the aim is to account for the heterogeneity in neuronal activity both within- and between-subjects. Then, we will discuss applications to cancer radiomics, an emerging discipline that promises to elucidate lesion phenotypes and tumor heterogeneity through the analysis of large amounts of quantitative imaging features that can be derived from medical images. We will show how a fully Bayesian probabilistic framework may help to characterize the heterogeneity of cancer tissue images obtained from CT scans and assist clinical decisions.
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