Titlebook: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology; Third International Seyed Mostafa Kia,Hassan Mohy-ud-Din,Ma

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A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Doquent cortex in brain tumor patients. Our method leverages convolutional layers to extract graph-based features from the dynamic connectivity matrices and a long-short term memory (LSTM) attention network to weight the relevant time points during classification. The final stage of our model employs

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Deep Learning for Non-invasive Cortical Potential Imagingiations in electric conductivity between different tissues distort the electric fields generated by cortical sources, resulting in smeared potential measurements on the scalp. One needs to solve an ill-posed inverse problem to recover the original neural activity. In this article, we present a gener

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SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification the disease. This task is challenging due to factors such as low signal-to-noise ratios, signal artefacts, high variance in seizure semiology among epileptic patients, and limited availability of clinical data. To overcome these challenges, in this paper, we present SeizureNet, a deep learning fram

Acumen

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Patch-Based Brain Age Estimation from?MR?Images This is a potential biomarker for neurodegeneration, e.g. as part of Alzheimer’s disease. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. Many studies have been proposed for the prediction o

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Large-Scale Unbiased Neuroimage Indexing via 3D GPU-SIFT Filtering and?Keypoint Maskingt feature transform (SIFT). The feature extraction is first represented as a shallow convolutional neural network with pre-computed filters, followed by a masked keypoint analysis. We use the implementation in order to investigate feature extraction for specific instance identification on natural no

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