標(biāo)題: Titlebook: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology; Third International Seyed Mostafa Kia,Hassan Mohy-ud-Din,Ma [打印本頁(yè)] 作者: miserly 時(shí)間: 2025-3-21 19:45
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology影響因子(影響力)
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology影響因子(影響力)學(xué)科排名
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology網(wǎng)絡(luò)公開(kāi)度
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology被引頻次
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology被引頻次學(xué)科排名
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology年度引用
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology年度引用學(xué)科排名
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology讀者反饋
書目名稱Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology讀者反饋學(xué)科排名
作者: Stress 時(shí)間: 2025-3-21 20:36 作者: ORBIT 時(shí)間: 2025-3-22 03:02
Samuel Budd,Prachi Patkee,Ana Baburamani,Mary Rutherford,Emma C. Robinson,Bernhard Kainzphysics. It is written by and for researchers who are primarily analysts or physicists, not algebraists or geometers. Not that we have eschewed the algebraic and geo- metric developments. But we wanted to present them in a concrete way and to show how the subject interacted with physics, geometry, a作者: conjunctiva 時(shí)間: 2025-3-22 05:27 作者: Cholecystokinin 時(shí)間: 2025-3-22 10:38
Matthias Wilms,Jordan J. Bannister,Pauline Mouches,M. Ethan MacDonald,Deepthi Rajashekar,S?nke Langnt is written by and for researchers who are primarily analysts or physicists, not algebraists or geometers. Not that we have eschewed the algebraic and geo- metric developments. But we wanted to present them in a concrete way and to show how the subject interacted with physics, geometry, and mechani作者: lesion 時(shí)間: 2025-3-22 14:36 作者: 改革運(yùn)動(dòng) 時(shí)間: 2025-3-22 18:18 作者: Veneer 時(shí)間: 2025-3-23 00:03
Tommaso Di Noto,Guillaume Marie,Sébastien Tourbier,Yasser Alemán-Gómez,Guillaume Saliou,Meritxell Bat is written by and for researchers who are primarily analysts or physicists, not algebraists or geometers. Not that we have eschewed the algebraic and geo- metric developments. But we wanted to present them in a concrete way and to show how the subject interacted with physics, geometry, and mechani作者: Epithelium 時(shí)間: 2025-3-23 01:54
Jianyuan Zhang,Feng Shi,Lei Chen,Zhong Xue,Lichi Zhang,Dahong Qianphysics. It is written by and for researchers who are primarily analysts or physicists, not algebraists or geometers. Not that we have eschewed the algebraic and geo- metric developments. But we wanted to present them in a concrete way and to show how the subject interacted with physics, geometry, a作者: Commentary 時(shí)間: 2025-3-23 09:37 作者: CHAR 時(shí)間: 2025-3-23 11:57 作者: 惡意 時(shí)間: 2025-3-23 15:09
Kyriaki-Margarita Bintsi,Vasileios Baltatzis,Arinbj?rn Kolbeinsson,Alexander Hammers,Daniel Rueckert of explanation or procedure that parts from a capacity of a system and shows how this capacity is exercised by the system by decomposing it into other sub-capacities that are implemented in the (parts of the) system. Our goal in this work is to examine the adequacy of this proposal. We provide a fo作者: 混沌 時(shí)間: 2025-3-23 18:10 作者: Cryptic 時(shí)間: 2025-3-23 22:15
Stefano Cerri,Andrew Hoopes,Douglas N. Greve,Mark Mühlau,Koen Van Leemputbook is aimed at those who wish to understand more about the molecular basis of life and how life on earth may change in coming centuries. Readers of this book will gain knowledge of how life began on Earth, the natural processes that have led to the great diversity of biological organisms that exis作者: Spangle 時(shí)間: 2025-3-24 04:50
Yannick Suter,Urspeter Knecht,Roland Wiest,Ekkehard Hewer,Philippe Schucht,Mauricio Reyes to study quality of life using data of the China Family Pan.This book examines subjective wellbeing in China in terms of life and job satisfaction from a longitudinal perspective during the last decade. Using quantitative methods, the research presented in this volume performed a longitudinal analy作者: inconceivable 時(shí)間: 2025-3-24 07:38
Hao Li,Huahong Zhang,Dewei Hu,Hans Johnson,Jeffrey D. Long,Jane S. Paulsen,Ipek Oguz to study quality of life using data of the China Family Pan.This book examines subjective wellbeing in China in terms of life and job satisfaction from a longitudinal perspective during the last decade. Using quantitative methods, the research presented in this volume performed a longitudinal analy作者: 節(jié)省 時(shí)間: 2025-3-24 13:33
Ling-Li Zeng,Christopher R. K. Ching,Zvart Abaryan,Sophia I. Thomopoulos,Kai Gao,Alyssa H. Zhu,Anjann school and they encounter digital technologies in various (learning) situations. We look in more detail into the life of schools, focusing on how Czech schools are equipped with digital technologies, how teachers use the digital technologies in their lessons, what the teachers’ skills and abilitie作者: allude 時(shí)間: 2025-3-24 15:22
Guy Leroy,Daniel Rueckert,Amir Alansaryt national and international levels.Benefits teacher educatoThis book describes and explains how digital technologies enter adolescents’ everyday life and learning in different contexts and environments. The book is based on research conducted in recent years in the Czech Republic, the results of wh作者: 高調(diào) 時(shí)間: 2025-3-24 20:26 作者: 容易做 時(shí)間: 2025-3-25 00:08
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion , the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we 作者: forecast 時(shí)間: 2025-3-25 05:40 作者: 友好 時(shí)間: 2025-3-25 09:25
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作者: Peristalsis 時(shí)間: 2025-3-25 14:38
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作者: GULF 時(shí)間: 2025-3-25 15:59 作者: reject 時(shí)間: 2025-3-25 20:59 作者: 引起痛苦 時(shí)間: 2025-3-26 03:59
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作者: Acumen 時(shí)間: 2025-3-26 04:30 作者: expire 時(shí)間: 2025-3-26 12:22
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作者: 玷污 時(shí)間: 2025-3-26 13:51
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作者: judiciousness 時(shí)間: 2025-3-26 18:56 作者: MINT 時(shí)間: 2025-3-26 22:04 作者: 不連貫 時(shí)間: 2025-3-27 04:08 作者: Obliterate 時(shí)間: 2025-3-27 06:48
Multiple Sclerosis Lesion Segmentation Using Longitudinal Normalization and Convolutional Recurrent d inflammatory activities are examined by longitudinal image analysis to support diagnosis and treatment decision. Automated lesion segmentation methods based on deep convolutional neural networks (CNN) have been proposed, but are not yet applied in the clinical setting. Typical CNNs working on cros作者: Loathe 時(shí)間: 2025-3-27 09:26 作者: 針葉 時(shí)間: 2025-3-27 13:48
A Deep Transfer Learning Framework for 3D Brain Imaging Based on Optimal Mass Transportuirements to optimize performance. In this study, we propose a deep transfer learning network based on Optimal Mass Transport (OMTNet) for 3D brain image classification using MRI scans from the UK Biobank. The major contributions of the OMTNet method include: a way to map?3D surface-based vertex-wis作者: 奇思怪想 時(shí)間: 2025-3-27 21:35 作者: Acetabulum 時(shí)間: 2025-3-28 01:29
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flowsg able to generate age-specific brain morphology templates that realistically represent the typical aging trend in a healthy population. This work is a step towards unified modeling of functional relationships between 3D brain morphology and clinical variables of interest with powerful normalizing f作者: 機(jī)警 時(shí)間: 2025-3-28 02:53
A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dional deep learning approaches and can identify bilateral language areas even when trained on left-hemisphere lateralized cases. Hence, our method may ultimately be useful for preoperative mapping in tumor patients.作者: inculpate 時(shí)間: 2025-3-28 09:16 作者: 劇本 時(shí)間: 2025-3-28 12:56
An Anatomically-Informed 3D CNN for Brain Aneurysm Classification with Weak Labelsbutions. To tackle this frequent scenario of inherently imbalanced, spatially skewed data sets, we propose a novel, anatomically-driven approach by using a multi-scale and multi-input 3D Convolutional Neural Network (CNN). We apply our model to 214 subjects (83 patients, 131 controls) who underwent 作者: 爭(zhēng)論 時(shí)間: 2025-3-28 16:27 作者: CLASP 時(shí)間: 2025-3-28 19:04
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classificationsification. We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the accuracy of smaller networks through knowledge distillation for applications with low-memory constraints.作者: 結(jié)束 時(shí)間: 2025-3-29 01:01 作者: AROMA 時(shí)間: 2025-3-29 03:29
Patch-Based Brain Age Estimation from?MR?Imagesrain age, leading to more anatomically driven and interpretable results, and thus confirming relevant literature which suggests that the ventricles and the hippocampus are the areas that are most informative. In addition, we leverage this knowledge in order to improve the overall performance on the 作者: placebo-effect 時(shí)間: 2025-3-29 09:24 作者: gruelling 時(shí)間: 2025-3-29 12:11 作者: 詩(shī)集 時(shí)間: 2025-3-29 15:50
Multiple Sclerosis Lesion Segmentation Using Longitudinal Normalization and Convolutional Recurrent rm Memory (C-LSTM) networks to incorporate the temporal dimension. To reduce scanner- and protocol dependent variations between single MRI exams, we propose a histogram normalization technique as pre-processing step. The ISBI 2015 challenge data was used for network training and cross-validation..We作者: endarterectomy 時(shí)間: 2025-3-29 20:31
Deep Voxel-Guided Morphometry (VGM): Learning Regional Brain Changes in Serial MRIolute Error and Gradient loss outperformed all other tested loss functions. Deep VGM maps showed high similarity to the original VGM maps (SSIM .). This was additionally confirmed by a neurologist analysing the MS lesions. Deep VGM resulted in a 3% lesion error rate compared to the original VGM appr作者: 談判 時(shí)間: 2025-3-30 02:28
A Deep Transfer Learning Framework for 3D Brain Imaging Based on Optimal Mass Transportategy to fuse all shape metrics and generate an ensemble classification. We tested the approach in a classification task conducted on 26k participants from the UK Biobank, using body mass index (BMI) thresholds as classification labels (normal vs. obese BMI). Ensemble classification accuracies of 72作者: right-atrium 時(shí)間: 2025-3-30 04:54 作者: hurricane 時(shí)間: 2025-3-30 11:00
and Jacobson address themselves to the mathematical community. This book is an attempt to synthesize the two points of view and address both audiences simultan978-1-4419-3077-4978-1-4757-1910-9Series ISSN 0066-5452 Series E-ISSN 2196-968X 作者: 云狀 時(shí)間: 2025-3-30 15:54
Samuel Budd,Prachi Patkee,Ana Baburamani,Mary Rutherford,Emma C. Robinson,Bernhard Kainz and Jacobson address themselves to the mathematical community. This book is an attempt to synthesize the two points of view and address both audiences simultan978-1-4419-3077-4978-1-4757-1910-9Series ISSN 0066-5452 Series E-ISSN 2196-968X 作者: Atmosphere 時(shí)間: 2025-3-30 19:02
Gabriele Amorosino,Denis Peruzzo,Pietro Astolfi,Daniela Redaelli,Paolo Avesani,Filippo Arrigoni,Eman and Jacobson address themselves to the mathematical community. This book is an attempt to synthesize the two points of view and address both audiences simultan978-1-4419-3077-4978-1-4757-1910-9Series ISSN 0066-5452 Series E-ISSN 2196-968X 作者: 不透明性 時(shí)間: 2025-3-30 21:45
Alexandra Razorenova,Nikolay Yavich,Mikhail Malovichko,Maxim Fedorov,Nikolay Koshev,Dmitry V. Dylov and Jacobson address themselves to the mathematical community. This book is an attempt to synthesize the two points of view and address both audiences simultan978-1-4419-3077-4978-1-4757-1910-9Series ISSN 0066-5452 Series E-ISSN 2196-968X 作者: floaters 時(shí)間: 2025-3-31 00:55 作者: 貴族 時(shí)間: 2025-3-31 07:54 作者: filicide 時(shí)間: 2025-3-31 10:51
Conference proceedings 2020ets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience...*The workshops were held virtually due to the COVID-19 pandemic..作者: 衰弱的心 時(shí)間: 2025-3-31 16:06 作者: Cervical-Spine 時(shí)間: 2025-3-31 20:00
Hao Li,Huahong Zhang,Dewei Hu,Hans Johnson,Jeffrey D. Long,Jane S. Paulsen,Ipek Oguzsatisfaction among the Chinese population over time. ??..Life and Job Satisfaction in China: Exploring Longitudinal Analysis with Mplus. will be of interest to sociologists, statisticians and applied researcher978-3-031-48697-5978-3-031-48695-1Series ISSN 2211-0550 Series E-ISSN 2211-0569 作者: 逢迎春日 時(shí)間: 2025-4-1 00:10 作者: GILD 時(shí)間: 2025-4-1 05:44
