Titlebook: Machine Learning in Clinical Neuroimaging; 5th International Wo Ahmed Abdulkadir,Deepti R. Bathula,Thomas Wolfers Conference proceedings 20

肥料

rods366

Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma which we call Boundary Distance Loss. The performance is evaluated in contrast to the direct intrameatal extrameatal segmentation task performance, i.e. the Baseline. Our proposed method, with the two-stage approach and the Boundary Distance Loss, achieved a Dice score of . and . for extrameatal an

mighty

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不透明

Lifestyle Factors That Promote Brain Structural Resilience in Individuals with Genetic Risk Factors a 3D convolutional neural network trained on T1w brain MRIs to identify the subset of genetically high-risk individuals with a substantially lower brain age than chronological age, which we interpret as resilient to neurodegeneration. We used association rule learning to identify sets of lifestyle

嘲弄

Augmenting Magnetic Resonance Imaging with Tabular Features for Enhanced and Interpretable Medial Tea consisting of T1-weighted brain MRI and tabular data encompassing brain region volumes, cortical thickness, and radiomics features. Our method outperforms various baselines considered, and its attention map on input images and feature importance scores on tabular data explains its reasoning.

使困惑

Automatic Lesion Analysis for Increased Efficiency in Outcome Prediction of Traumatic Brain Injurycorresponding lesion statistics as inputs for an extended TBI outcome prediction model. We compare the predictive power of our proposed features to the Marshall score, independently and when paired with classic TBI biomarkers. We find that automatically extracted quantitative CT features perform sim

APRON

Autism Spectrum Disorder Classification Based on Interpersonal Neural Synchrony: Can Classification e proposed method differs from existing approaches in that it is more suitable to capture social interaction deficits on a neural level and is applicable to young children and infants. First results from functional near-infrared spectroscopy data indicate potential predictive capacities of a task-ag

maladorit

fMRI-S4: Learning Short- and Long-Range Dynamic fMRI Dependencies Using 1D Convolutions and State Spenotypes and psychiatric disorders from the timecourses of resting-state functional magnetic resonance imaging scans. fMRI-S4 capture short- and long-range temporal dependencies in the signal using 1D convolutions and the recently introduced state-space models .. The proposed architecture is lightwe

aspect

Matías Bossa,Abel Díaz Berenguer,Hichem Sahliraduate and advanced undergraduate students alike will find in this book a solid yet approachable guide that will help them continue their studies with confidence..978-3-030-61824-7Series ISSN 2524-6755 Series E-ISSN 2524-6763