標(biāo)題: Titlebook: Machine Learning in Clinical Neuroimaging; 6th International Wo Ahmed Abdulkadir,Deepti R. Bathula,Yiming Xiao Conference proceedings 2023 [打印本頁(yè)] 作者: ED431 時(shí)間: 2025-3-21 19:12
書(shū)目名稱Machine Learning in Clinical Neuroimaging影響因子(影響力)
書(shū)目名稱Machine Learning in Clinical Neuroimaging影響因子(影響力)學(xué)科排名
書(shū)目名稱Machine Learning in Clinical Neuroimaging網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Machine Learning in Clinical Neuroimaging網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Machine Learning in Clinical Neuroimaging被引頻次
書(shū)目名稱Machine Learning in Clinical Neuroimaging被引頻次學(xué)科排名
書(shū)目名稱Machine Learning in Clinical Neuroimaging年度引用
書(shū)目名稱Machine Learning in Clinical Neuroimaging年度引用學(xué)科排名
書(shū)目名稱Machine Learning in Clinical Neuroimaging讀者反饋
書(shū)目名稱Machine Learning in Clinical Neuroimaging讀者反饋學(xué)科排名
作者: habile 時(shí)間: 2025-3-21 23:02
VesselShot: Few-shot Learning for?Cerebral Blood Vessel Segmentationerages knowledge from a few annotated support images and mitigates the scarcity of labeled data and the need for extensive annotation in cerebral blood vessel segmentation. We evaluated the performance of VesselShot using the publicly available TubeTK dataset for the segmentation task, achieving a mean Dice coefficient (DC) of ..作者: deactivate 時(shí)間: 2025-3-22 01:44
Learning Sequential Information in?Task-Based fMRI for?Synthetic Data Augmentationl information. The synthetic images are evaluated from multiple perspectives including visualizations and an autism spectrum disorder (ASD) classification task. The results show that the synthetic task-based fMRI can provide effective data augmentation in learning the ASD classification task.作者: MAOIS 時(shí)間: 2025-3-22 07:24 作者: 排出 時(shí)間: 2025-3-22 10:54
Stroke Outcome and Evolution Prediction from CT Brain Using a Spatiotemporal Diffusion Autoencodera dataset consisting of 5,824 CT images from 3,573 patients across two medical centers with minimal labels. Comparative experiments show that our method achieves the best performance for predicting next-day severity and functional outcome at discharge.作者: 頑固 時(shí)間: 2025-3-22 16:29
Morphological Versus Functional Network Organization: A Comparison Between Structural Covariance Neten morphological and functional networks at the lowest rank (2). Morphology-function network commonality was retained across all ranks in the visual cortex, but broader network organization diverged between morphology and function at higher ranks.作者: 積習(xí)難改 時(shí)間: 2025-3-22 19:18
0302-9743 ld in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023.?.The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions..The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) aims to bring together the top re作者: 腐蝕 時(shí)間: 2025-3-23 00:56 作者: Missile 時(shí)間: 2025-3-23 04:38 作者: granite 時(shí)間: 2025-3-23 09:15 作者: 空洞 時(shí)間: 2025-3-23 11:12 作者: doxazosin 時(shí)間: 2025-3-23 17:09
Elizabeth Haddad,Myrl G. Marmarelis,Talia M. Nir,Aram Galstyan,Greg Ver Steeg,Neda Jahanshadthe abstract mathematical approach of Helgason or Jacobson. Gilmore and Wybourne address themselvesto the physics community whereas Helgason and Jacobson address themselves to the mathematical community. This book is an attempt to synthesize the two points of view and address both audiences simultan作者: white-matter 時(shí)間: 2025-3-23 20:17 作者: barium-study 時(shí)間: 2025-3-24 01:54 作者: 闖入 時(shí)間: 2025-3-24 03:53 作者: 鐵塔等 時(shí)間: 2025-3-24 07:11 作者: Freeze 時(shí)間: 2025-3-24 11:25 作者: 積云 時(shí)間: 2025-3-24 15:47 作者: 舊石器 時(shí)間: 2025-3-24 22:08 作者: CORE 時(shí)間: 2025-3-25 00:22 作者: Interferons 時(shí)間: 2025-3-25 07:10 作者: FACT 時(shí)間: 2025-3-25 08:29
A Three-Player GAN for?Super-Resolution in?Magnetic Resonance Imaging the GAN, as well as a relativistic GAN loss function and an updating feature extractor as the third player during the training process. Our experiments demonstrate that our method produces highly accurate results using very few training samples. Specifically, we show that we need less than 30 train作者: 魔鬼在游行 時(shí)間: 2025-3-25 15:29
Cross-Attention for?Improved Motion Correction in?Brain PETers: HRRT without time of flight (ToF) and mCT with ToF. Compared with traditional and deep learning benchmarks, our network improved the performance of motion correction by 58% and 26% in translation and rotation, respectively, in multi-subject testing in HRRT studies. In mCT studies, our approach 作者: 記成螞蟻 時(shí)間: 2025-3-25 16:51 作者: 擁護(hù)者 時(shí)間: 2025-3-25 23:16 作者: 可忽略 時(shí)間: 2025-3-26 02:37
Causal Sensitivity Analysis for Hidden Confounding: Modeling the Sex-Specific Role of Diet on the Agwidespread and also robust to hidden confounds in males compared to females. Specific dietary components, including a higher consumption of whole grains, vegetables, dairy, and vegetable oils as well as a lower consumption of meat appears to be more beneficial to brain structure (e.g., greater thick作者: 啪心兒跳動(dòng) 時(shí)間: 2025-3-26 08:16 作者: rods366 時(shí)間: 2025-3-26 12:29 作者: medieval 時(shí)間: 2025-3-26 16:24 作者: Expurgate 時(shí)間: 2025-3-26 19:36 作者: BOOM 時(shí)間: 2025-3-26 21:23
Deep Attention Assisted Multi-resolution Networks for?the?Segmentation of?White Matter Hyperintensitredict white matter hyperintensities from a registered pair of T1 and T2-weighted postmortem MRI scans of five Unet architectures: the original Unet, DoubleUNet, Attention UNet, Multiresolution UNet, and a new architecture specifically designed for the task. A detailed comparison between these five 作者: cloture 時(shí)間: 2025-3-27 01:08 作者: cushion 時(shí)間: 2025-3-27 06:09
raduate 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 作者: aspersion 時(shí)間: 2025-3-27 09:33
Michael Tran Duong,Sandhitsu R. Das,Pulkit Khandelwal,Xueying Lyu,Long Xie,Paul A. Yushkevich,Alzheiraduate 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 作者: Explicate 時(shí)間: 2025-3-27 15:39
Reagan Dugan,Owen Carmichaelraduate 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 作者: 沒(méi)花的是打擾 時(shí)間: 2025-3-27 18:46 作者: AVANT 時(shí)間: 2025-3-28 01:56 作者: grenade 時(shí)間: 2025-3-28 05:49 作者: Tonometry 時(shí)間: 2025-3-28 08:50
Lucas Mahler,Qi Wang,Julius Steiglechner,Florian Birk,Samuel Heczko,Klaus Scheffler,Gabriele Lohmann 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: 忍耐 時(shí)間: 2025-3-28 12:24 作者: 偏離 時(shí)間: 2025-3-28 16:20
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620661.jpg作者: 固定某物 時(shí)間: 2025-3-28 20:28 作者: slipped-disk 時(shí)間: 2025-3-29 02:34
Machine Learning in Clinical Neuroimaging978-3-031-44858-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 歌唱隊(duì) 時(shí)間: 2025-3-29 04:10 作者: GROVE 時(shí)間: 2025-3-29 08:06
Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease wilationship between tau and neuronal hypometabolism with positron emission tomography (PET) has been studied by T/N regression models, there has been limited application of image-to-image translation to compare between AD biomarker domains. We optimize a contrastive learning (CL) generative adversari作者: 四牛在彎曲 時(shí)間: 2025-3-29 14:51
Multi-shell dMRI Estimation from Single-Shell Data via Deep Learninges compartmental modeling of brain tissues as well as enhanced estimation of white matter fiber orientations via the orientation distribution function (ODF). However, multi-shell dMRI acquisitions are time consuming, expensive and difficult in certain clinical populations. We present a method to est作者: Lacunar-Stroke 時(shí)間: 2025-3-29 15:35 作者: Ostrich 時(shí)間: 2025-3-29 21:02 作者: Accolade 時(shí)間: 2025-3-30 01:08
VesselShot: Few-shot Learning for?Cerebral Blood Vessel Segmentationar network from different imaging modalities, deep learning (DL) has emerged as a promising approach. However, existing DL methods often depend on proprietary datasets and extensive manual annotation. Moreover, the availability of pre-trained networks specifically for medical domains and 3D volumes 作者: alliance 時(shí)間: 2025-3-30 07:48
WaveSep: A Flexible Wavelet-Based Approach for?Source Separation in?Susceptibility Imaginge biological functions and health conditions of the brain. However, general and flexible deep-learning-based tools that can provide this information in humans . are limited. For instance, the state-of-the-art deep-learning-based source separation method in quantitative susceptibility mapping (QSM) d作者: 啤酒 時(shí)間: 2025-3-30 08:31
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作者: 忘恩負(fù)義的人 時(shí)間: 2025-3-30 16:06 作者: 破譯 時(shí)間: 2025-3-30 20:02
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作者: prosthesis 時(shí)間: 2025-3-30 23:58
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作者: homocysteine 時(shí)間: 2025-3-31 03:40
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作者: amorphous 時(shí)間: 2025-3-31 07:27 作者: arrogant 時(shí)間: 2025-3-31 10:50
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作者: SLAG 時(shí)間: 2025-3-31 13:45 作者: Factorable 時(shí)間: 2025-3-31 17:33
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作者: 熄滅 時(shí)間: 2025-4-1 01:34
