Titlebook: Machine Learning in Clinical Neuroimaging; 6th International Wo Ahmed Abdulkadir,Deepti R. Bathula,Yiming Xiao Conference proceedings 2023

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Joint Estimation of Neural Events and Hemodynamic Response Functions from Task fMRI via Convolutionaunctions (HRF) can enable new insights into functional connectivity, task activation, and neurovascular coupling in health and disease. Current methods for this problem handle time series of either temporally isolated events or extended blocks of continuous events but not both; and they constrain th

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Causal Sensitivity Analysis for Hidden Confounding: Modeling the Sex-Specific Role of Diet on the Agalth for an individual is not clear. Clinical trials allow for the modification of a single variable at a time, but these may not generalize to populations due to uncaptured confounding effects. Large scale epidemiological studies can be leveraged to robustly model associations that can be specifica

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MixUp Brain-Cortical Augmentations in Self-supervised Learning general population, new learning strategies have emerged. In particular, deep representation learning consists of training a model via pretext tasks that can be used to solve downstream clinical problems of interest. More recently, self-supervised learning provides a rich framework for learning rep

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Brain Age Prediction Based on Head Computed Tomography Segmentationspite the widespread availability of head computed tomography (CT) images in clinical settings, limited research has been dedicated to predicting brain age within this modality, often constrained to narrow age ranges or substantial disparities between predicted and chronological age. To address this

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Copy Number Variation Informs fMRI-Based Prediction of Autism Spectrum Disorderta from widely varying platforms, e.g., neuroimaging, genetics, and clinical characterization. Prior neuroimaging-genetic analyses often apply naive feature concatenation approaches in data-driven work or use the findings from one modality to guide posthoc analysis of another, missing the opportunit

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Stroke Outcome and Evolution Prediction from CT Brain Using a Spatiotemporal Diffusion Autoencoder individualizing clinical decision-making leading to better outcomes. However, despite a plethora of attempts and the rich data provided by neuroimaging, modelling the ultimate fate of brain tissue remains a challenging task. In this work, we apply recent ideas in the field of diffusion probabilisti

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