標(biāo)題: Titlebook: Machine Learning in Medical Imaging; 7th International Wo Li Wang,Ehsan Adeli,Heung-Il Suk Conference proceedings 2016 Springer Internation [打印本頁] 作者: deduce 時(shí)間: 2025-3-21 18:03
書目名稱Machine Learning in Medical Imaging影響因子(影響力)
書目名稱Machine Learning in Medical Imaging影響因子(影響力)學(xué)科排名
書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度
書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning in Medical Imaging被引頻次
書目名稱Machine Learning in Medical Imaging被引頻次學(xué)科排名
書目名稱Machine Learning in Medical Imaging年度引用
書目名稱Machine Learning in Medical Imaging年度引用學(xué)科排名
書目名稱Machine Learning in Medical Imaging讀者反饋
書目名稱Machine Learning in Medical Imaging讀者反饋學(xué)科排名
作者: 有權(quán)威 時(shí)間: 2025-3-21 21:28
0302-9743 dical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. ..The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions. ..The main aim of this workshop is to help advance scientific research within the broad field of作者: Arb853 時(shí)間: 2025-3-22 02:29 作者: 打包 時(shí)間: 2025-3-22 06:07 作者: BIAS 時(shí)間: 2025-3-22 11:00
Cross-Modality Anatomical Landmark Detection Using Histograms of Unsigned Gradient Orientations andof CT, MRI T1 and MRI T2 scans of the head. Each cohort is made up of 40 training and 20 test scans, making 180 scans in?total. A cross-modality mean landmark error of 5.27?mm is achieved, compared to single-modality error of 4.07?mm.作者: beta-carotene 時(shí)間: 2025-3-22 14:25 作者: 放牧 時(shí)間: 2025-3-22 19:06
Conference proceedings 2016bmissions. ..The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging..作者: mettlesome 時(shí)間: 2025-3-22 23:22
0302-9743 machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging..978-3-319-47156-3978-3-319-47157-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 攤位 時(shí)間: 2025-3-23 01:41 作者: 反饋 時(shí)間: 2025-3-23 05:32 作者: 過分自信 時(shí)間: 2025-3-23 11:26
Learning Global and Cluster-Specific Classifiers for Robust Brain Extraction in MR Data,e best performance with statistically significant improvement of the cluster-specific method (with a Dice coefficient of 97.6?±?0.4?% and an average surface distance of 0.8?±?0.1?mm) over the global method.作者: 付出 時(shí)間: 2025-3-23 17:10
Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern De on a dataset of 533 patients using five-fold cross-validation, achieving high area-under-curve (AUC) scores of 0.982, 0.972, 0.893 and 0.993 for Ground Glass, Reticular, Honeycomb and Emphysema, respectively. As such, our work represents an important step forward in providing clinically effective ILD detection.作者: DOLT 時(shí)間: 2025-3-23 18:30 作者: Talkative 時(shí)間: 2025-3-23 23:09 作者: Jacket 時(shí)間: 2025-3-24 05:51 作者: GEST 時(shí)間: 2025-3-24 09:24 作者: Venules 時(shí)間: 2025-3-24 11:46 作者: Hemiparesis 時(shí)間: 2025-3-24 15:54 作者: anaerobic 時(shí)間: 2025-3-24 19:01 作者: infantile 時(shí)間: 2025-3-24 23:27 作者: Ambulatory 時(shí)間: 2025-3-25 04:44
Dual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images,mical labeling methods that can respect temporal consistency between images. However, the characteristics of such longitudinal images and how they lodge into the image manifold are often neglected in existing labeling methods. Indeed, most of them independently align atlases to each target time-poin作者: kindred 時(shí)間: 2025-3-25 10:47
,Joint Discriminative and Representative Feature Selection for Alzheimer’s Disease Diagnosis,d to AD progression, many feature selection methods have been proposed to identify informative features (.brain regions) to build an accurate prediction model. These methods mostly only focus on the feature-target relationship to select features which are discriminative to the targets (.diagnosis la作者: nugatory 時(shí)間: 2025-3-25 14:38 作者: 增減字母法 時(shí)間: 2025-3-25 17:07 作者: Conclave 時(shí)間: 2025-3-25 20:16 作者: Estimable 時(shí)間: 2025-3-26 01:30
,Deep Ensemble Sparse Regression Network for Alzheimer’s Disease Diagnosis,small number of samples. In this paper, we propose a novel framework that utilizes sparse regression models as . learner and builds a deep convolutional neural network for clinical decision making. Specifically, we first train multiple sparse regression models, each of which has different values of 作者: Perceive 時(shí)間: 2025-3-26 06:47
Learning Representation for Histopathological Image with Quaternion Grassmann Average Network,w unsupervised feature learning algorithm for images via a simple deep network architecture. However, PCA is sensitive to noise and outliers, which may depress the representation learning of PCANet. Grassmann averages (GA) is a newly proposed dimensionality reduction algorithm, which is more robust 作者: 暴發(fā)戶 時(shí)間: 2025-3-26 09:09 作者: ectropion 時(shí)間: 2025-3-26 12:45 作者: Neuralgia 時(shí)間: 2025-3-26 18:35
Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Deolutions rely on manually provided regions of interest, limiting their clinical usefulness. In addition, no work has yet focused on predicting more than one ILD from the same CT slice, despite the frequency of such occurrences. To address these limitations, we propose two variations of multi-label d作者: 做作 時(shí)間: 2025-3-26 22:50 作者: 懦夫 時(shí)間: 2025-3-27 01:56 作者: PAN 時(shí)間: 2025-3-27 07:00 作者: maintenance 時(shí)間: 2025-3-27 11:12 作者: 蠟燭 時(shí)間: 2025-3-27 14:37
Michael Gadermayr,Barbara Mara Klinkhammer,Peter Boor,Dorit Merhof traditional knowledge, culture and language – can be extended and strengthened by (1) the new, integrated methodology of Lifelong Action Learning (LAL), and (2) new approaches to learning and development as exemplified by the system of GULL (Global University for Lifelong Learning). The GULL system作者: 類人猿 時(shí)間: 2025-3-27 18:04 作者: 全神貫注于 時(shí)間: 2025-3-28 00:56 作者: Junction 時(shí)間: 2025-3-28 05:08
Jessie Thomson,Terence O’Neill,David Felson,Tim Cootesed by (1) the new, integrated methodology of Lifelong Action Learning (LAL), and (2) new approaches to learning and development as exemplified by the system of GULL (Global University for Lifelong Learning). The GULL system harnesses the potential of people to bring about positive change together, c作者: 束以馬具 時(shí)間: 2025-3-28 06:29 作者: 改變 時(shí)間: 2025-3-28 12:32 作者: 長矛 時(shí)間: 2025-3-28 18:11 作者: Detoxification 時(shí)間: 2025-3-28 19:07 作者: Substance-Abuse 時(shí)間: 2025-3-28 23:46
Yan Wang,Xi Wu,Guangkai Ma,Zongqing Ma,Ying Fu,Jiliu Zhoud interna- tional level. At a time when education and lifelong learning are increasingly merging into one process, it is important to examine the ways in which edu- cational policies and practices are evolving. Consequently, we invited a variety of contributors, both men and women, coming from diffe作者: Institution 時(shí)間: 2025-3-29 03:53
Heung-Il Suk,Dinggang Shen major trends in the European discussion on Lifelong Learning. The author’s main focus is the pedagogic view of the idea. She gives an overview of the development and the main points of the European discussion (chapter 1 and 2), analyses concepts of Lifelong Learning in school (3) and in the field o作者: dithiolethione 時(shí)間: 2025-3-29 09:46
Jinjie Wu,Jun Shi,Shihui Ying,Qi Zhang,Yan Linds in the European discussion on Lifelong Learning. The author’s main focus is the pedagogic view of the idea. She gives an overview of the development and the main points of the European discussion (chapter 1 and 2), analyses concepts of Lifelong Learning in school (3) and in the field of adult ed作者: 現(xiàn)實(shí) 時(shí)間: 2025-3-29 11:55
Yuan Liu,Hasan E. ?etingül,Benjamin L. Odry,Mariappan S. Nadarta should provide a good measurement of who participates, for what reasons and in what types of education and training activities. But furthermore, it is also crucial that data about education and training opportunities and social policy indicators are available, which can be matched with the indivi作者: 傾聽 時(shí)間: 2025-3-29 16:06
Alison O’Neil,Mohammad Dabbah,Ian Pooleta should provide a good measurement of who participates, for what reasons and in what types of education and training activities. But furthermore, it is also crucial that data about education and training opportunities and social policy indicators are available, which can be matched with the indivi作者: chemical-peel 時(shí)間: 2025-3-29 22:06 作者: 撫慰 時(shí)間: 2025-3-30 00:14 作者: 微不足道 時(shí)間: 2025-3-30 07:17
Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph, hyperedges which make the separation of two groups by the learned data representation be in the best consensus with the observed clinical labels. We deploy our method to find high order childhood autism biomarkers from rs-fMRI images. Promising results have been obtained from comprehensive evaluati作者: 正式演說 時(shí)間: 2025-3-30 09:17
Bilateral Regularization in Reproducing Kernel Hilbert Spaces for Discontinuity Preserving Image Res bilateral regularizer in order to guarantee a finite dimensional solution. In addition, we build direction-dependent basis functions within the SKM framework in order to elongate the transformations along the potential sliding organ boundaries. In the experiments, we evaluate the registration resu作者: Militia 時(shí)間: 2025-3-30 15:16 作者: intricacy 時(shí)間: 2025-3-30 18:53
Learning Appearance and Shape Evolution for Infant Image Registration in the First Year of Life,ppearance change between two new infant subjects. After that, it becomes much easier to deploy any conventional image registration method to complete the remaining registration since the above challenges for current state-of-the-art registration methods have been solved successfully. We apply our pr作者: insightful 時(shí)間: 2025-3-30 23:51 作者: 書法 時(shí)間: 2025-3-31 02:35 作者: guzzle 時(shí)間: 2025-3-31 07:07 作者: 河流 時(shí)間: 2025-3-31 10:06 作者: scotoma 時(shí)間: 2025-3-31 16:12 作者: 散開 時(shí)間: 2025-3-31 18:56
,Deep Ensemble Sparse Regression Network for Alzheimer’s Disease Diagnosis,nal neural network by taking the target-level representations as input. Our deep network learns to optimally fuse the predicted response variables, ., target-level representations, from the same sparse response model(s) and also those from the neighboring sparse response models. To our best knowledg作者: 大包裹 時(shí)間: 2025-4-1 01:36 作者: epicondylitis 時(shí)間: 2025-4-1 04:47
Chen Zu,Yue Gao,Brent Munsell,Minjeong Kim,Ziwen Peng,Yingying Zhu,Wei Gao,Daoqiang Zhang,Dinggang Sssor Mary Brydon-Miller, USA I was sold on this book from the foreword – the need to rethink how we think about and ‘do’ education is emerging as a ‘hot topic’ among academics.Professor Lesley Wood, South AfricaAll development 978-94-6209-389-8作者: acrobat 時(shí)間: 2025-4-1 06:20 作者: cacophony 時(shí)間: 2025-4-1 13:51 作者: 合乎習(xí)俗 時(shí)間: 2025-4-1 17:27 作者: 人充滿活力 時(shí)間: 2025-4-1 21:16
Gerard Sanroma,Oualid M. Benkarim,Gemma Piella,Miguel ángel González Ballesterboth deeply inspirational and highly practical all at the same time.Professor Mary Brydon-Miller, USA I was sold on this book from the foreword – the need to rethink how we think about and ‘do’ education is emerging as a ‘hot topic’ among academics.Professor Lesley Wood, South AfricaAll development
