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Titlebook: Machine Learning in Clinical Neuroimaging; 4th International Wo Ahmed Abdulkadir,Seyed Mostafa Kia,Thomas Wolfers Conference proceedings 20

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41#
發(fā)表于 2025-3-28 16:57:05 | 只看該作者
Qiang Ma,Emma C. Robinson,Bernhard Kainz,Daniel Rueckert,Amir Alansaryocio-economic conditions of rural households. Lastly, it examines the relative performance of fifteen major states of India in terms of education, health and human development. An important feature of the book is that it approaches these issues, applying rigorously advanced econometric methods, and
42#
發(fā)表于 2025-3-28 21:52:46 | 只看該作者
43#
發(fā)表于 2025-3-29 01:12:56 | 只看該作者
Kai-Cheng Chuang,Sreekrishna Ramakrishnapillai,Lydia Bazzano,Owen T. Carmichaelocio-economic conditions of rural households. Lastly, it examines the relative performance of fifteen major states of India in terms of education, health and human development. An important feature of the book is that it approaches these issues, applying rigorously advanced econometric methods, and
44#
發(fā)表于 2025-3-29 05:32:26 | 只看該作者
45#
發(fā)表于 2025-3-29 08:10:05 | 只看該作者
Towards Self-explainable Classifiers and?Regressors in Neuroimaging with?Normalizing Flowsights the explainability capabilities of the proposed models and shows that they achieve a similar level of accuracy as standard convolutional neural networks for image-based brain age regression and brain sex classification tasks.
46#
發(fā)表于 2025-3-29 14:52:19 | 只看該作者
MRI Image Registration Considerably Improves CNN-Based Disease Classificationear registration was found. The dataset split, although carefully matched for age and sex, affects the classifier performance strongly, suggesting that some subjects are easier to classify than others, possibly due to different clinical manifestations of AD and varying rates of disease progression.
47#
發(fā)表于 2025-3-29 17:45:47 | 只看該作者
48#
發(fā)表于 2025-3-29 22:03:57 | 只看該作者
49#
發(fā)表于 2025-3-30 03:22:01 | 只看該作者
50#
發(fā)表于 2025-3-30 06:22:52 | 只看該作者
Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellationand highest variance included areas within the medial frontal lobe, lateral occipital pole and insula. Qualitatively, our results suggest that more work is needed before geometric deep learning methods are capable of fully capturing atypical cortical topographies such as those seen in area 55b. Howe
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