標(biāo)題: Titlebook: Machine Learning in Medical Imaging; 8th International Wo Qian Wang,Yinghuan Shi,Kenji Suzuki Conference proceedings 2017 Springer Internat [打印本頁] 作者: 清楚明確 時(shí)間: 2025-3-21 17:50
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: Ceramic 時(shí)間: 2025-3-21 23:42
0302-9743 ical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. .The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad f作者: detach 時(shí)間: 2025-3-22 01:42 作者: alcohol-abuse 時(shí)間: 2025-3-22 05:16
Detection and Localization of Drosophila Egg Chambers in Microscopy Images,ximum likelihood ellipse model fitting. Our proposal is able to detect 96% of human-expert annotated egg chambers at relevant developmental stages with less than 1% false-positive rate, which is adequate for the further analysis.作者: 起波瀾 時(shí)間: 2025-3-22 08:47 作者: 咒語 時(shí)間: 2025-3-22 15:09
Automatic Classification of Proximal Femur Fractures Based on Attention Models,proximal femur fractures. We provide a detailed quantitative and qualitative validation on a dataset of 1000 images and report high accuracy with regard to inter-expert correlation values reported in the literature.作者: 肥料 時(shí)間: 2025-3-22 19:33
Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble,h convolutional long short-term memory (CLSTM) to extract voxel labels. Enhanced slice-wise label consistency is ensured, leading to improved segmentation stability and accuracy. We apply our model on ADNI dataset, and demonstrate that our proposed model outperforms the state-of-the-art solutions.作者: Melatonin 時(shí)間: 2025-3-22 21:43 作者: 尾隨 時(shí)間: 2025-3-23 05:15 作者: 法官 時(shí)間: 2025-3-23 06:59 作者: COST 時(shí)間: 2025-3-23 10:44
Dakai Jin,Ziyue Xu,Adam P. Harrison,Kevin George,Daniel J. Molluraluding case studies, best practices and methodologies in env.This book demonstrates the application of Life-cycle Cost Approach (LCCA) in the management of infrastructure and other investment projects in the context of developing countries. The main goal is to identify potential opportunities for th作者: Abduct 時(shí)間: 2025-3-23 14:16 作者: CAJ 時(shí)間: 2025-3-23 19:27
Jun Wang,Qian Wang,Shitong Wang,Dinggang Shen life.Asks whether personal immortality is possible.What are life and death? Is it possible to understand their essence and give clear definitions? Countless books and articles have been devoted to trying to answer these intriguing questions. However, there are still no definite and generally accept作者: obeisance 時(shí)間: 2025-3-24 01:37 作者: Frisky 時(shí)間: 2025-3-24 04:00 作者: Cardioversion 時(shí)間: 2025-3-24 08:12
Detection and Localization of Drosophila Egg Chambers in Microscopy Images,is methods require manual segmentation of individual egg chambers, which is a difficult and time-consuming task. We present an image processing pipeline to detect and localize Drosophila egg chambers that consists of the following steps: (i) superpixel-based image segmentation into relevant tissue c作者: 引起 時(shí)間: 2025-3-24 13:39
Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Cafication is to perform non-contrasted coronary computed-tomography (CCT) on a patient and present the resulting image to an expert, who then uses this to label CAC in a tedious and time-consuming process. To improve this situation, we present an automatic CAC labeling system with high clinical pract作者: 助記 時(shí)間: 2025-3-24 16:26
Atlas of Classifiers for Brain MRI Segmentation,asets taking into account both the imaging data and the corresponding labels. It is therefore more informative than the classical probabilistic atlas and more economical than the popular multi-atlas approaches, which require large memory consumption and high computational complexity for each segment作者: Meditate 時(shí)間: 2025-3-24 19:51
Dictionary Learning and Sparse Coding-Based Denoising for High-Resolution Task Functional Connectiv the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse represe作者: Benign 時(shí)間: 2025-3-24 23:19 作者: ALERT 時(shí)間: 2025-3-25 05:02
Multi-factorial Age Estimation from Skeletal and Dental MRI Volumes, asylum seekers lacking valid identification documents. In this work we propose automatic multi-factorial age estimation methods based on MRI data to extend the maximal age range from 19 years, as commonly used for age assessment based on hand bones, up?to 25 years, when combined with wisdom teeth a作者: 血友病 時(shí)間: 2025-3-25 08:36
Automatic Classification of Proximal Femur Fractures Based on Attention Models,ndard. We decompose the problem into the localization of the region-of-interest (ROI) and the classification of the localized region. Our solution relies on current advances in multi-task end-to-end deep learning. More specifically, we adapt an attention model known as Spatial Transformer (ST) to le作者: expdient 時(shí)間: 2025-3-25 12:16
Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation,xels. A single joint forest classifier is then trained on all the images, where (a) the supervoxel indices are used as labels for the voxels, (b) a joint node optimisation is done using training samples from all the images, and (c) in each leaf node, a distinct posterior distribution is stored per i作者: 完整 時(shí)間: 2025-3-25 17:47
Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble,f 2D convolutional operations, as well the inter-slice dependence within 3D volumes, our model stacks fully convolutional neural networks (CNN) through convolutional long short-term memory (CLSTM) to extract voxel labels. Enhanced slice-wise label consistency is ensured, leading to improved segmenta作者: Nerve-Block 時(shí)間: 2025-3-25 23:34
STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion, emergency situations. While cerebral CTP is capable of quantifying the blood flow dynamics by continuous scanning at a focused region of the brain, the associated excessive radiation increases the patients’ risk levels of developing cancer. To reduce the necessary radiation dose in CTP, decreasing 作者: Negligible 時(shí)間: 2025-3-26 01:31
,Classification of Alzheimer’s Disease by Cascaded Convolutional Neural Networks Using PET Images,ion Tomography (PET) is a functional imaging modality which can help physicians to predict AD. In recent years, machine learning methods have been widely studied on analysis of PET brain images for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the ha作者: bisphosphonate 時(shí)間: 2025-3-26 05:11
Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images,n assessments in clinical orthodontics. However, the registration by the traditional large-scale nonlinear optimization is time-consuming for the craniofacial CBCT images. The supervised random forest is known for its fast online performance, thought the limited training data impair the generalizati作者: Vulnerary 時(shí)間: 2025-3-26 11:15 作者: MOTIF 時(shí)間: 2025-3-26 13:38
Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementi.e., a multi-status dementia diagnosis problem. Multimodality neuroimaging data such as MRI and PET provide valuable insights to abnormalities, and genetic data such as Single Nucleotide Polymorphism (SNP) provide information about a patient’s AD risk factors. When used in conjunction, AD diagnosis 作者: 昏睡中 時(shí)間: 2025-3-26 20:38
3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Datst and noise, especially at peripheral branches, it is often challenging for automatic methods to strike a balance between extracting deeper airway branches and avoiding leakage to the surrounding parenchyma. Meanwhile, manual annotations are extremely time consuming for the airway tree, which inhib作者: lymphoma 時(shí)間: 2025-3-26 23:50
Efficient Groupwise Registration for Brain MRI by Fast Initialization,alysis. However, it is generally computationally expensive for performing groupwise registration on a large set of images. To alleviate this issue, we propose to utilize a fast initialization technique for speeding up the groupwise registration. Our main idea is to generate a set of simulated brain 作者: NIP 時(shí)間: 2025-3-27 04:29
Sparse Multi-view Task-Centralized Learning for ASD Diagnosis,iew task-centralized (Sparse-MVTC) classification method for computer-assisted diagnosis of ASD. In particular, since ASD is known to be age- and sex-related, we partition all subjects into different groups of age/sex, each of which can be treated as a classification task to learn. Meanwhile, we ext作者: FATAL 時(shí)間: 2025-3-27 09:00
Dakai Jin,Ziyue Xu,Adam P. Harrison,Kevin George,Daniel J. Molluraiences, it seeks to broaden the application of LCCA, which is often limited to specific phases of the life-cycle with little or no weight given to environmental aspects..The aim of the book is to mainstream LCC978-3-319-06286-0978-3-319-06287-7Series ISSN 2191-5547 Series E-ISSN 2191-5555 作者: 的’ 時(shí)間: 2025-3-27 13:00
Marie Bieth,Esther Alberts,Markus Schwaiger,Bjoern Menze作者: Immunization 時(shí)間: 2025-3-27 16:32
Sylvester Chiang,Sharmila Balasingham,Lara Richmond,Belinda Curpen,Mia Skarpathiotakis,Anne Martel作者: Minikin 時(shí)間: 2025-3-27 19:08 作者: 用肘 時(shí)間: 2025-3-28 00:47
Boris Kodner,Shiri Gordon,Jacob Goldberger,Tammy Riklin Raviv作者: tangle 時(shí)間: 2025-3-28 05:48 作者: Iniquitous 時(shí)間: 2025-3-28 08:03
Jorge Samper-González,Ninon Burgos,Sabrina Fontanella,Hugo Bertin,Marie-Odile Habert,Stanley Durrlem作者: Traumatic-Grief 時(shí)間: 2025-3-28 13:09
Darko ?tern,Philipp Kainz,Christian Payer,Martin Urschler作者: BRAND 時(shí)間: 2025-3-28 16:59
Anees Kazi,Shadi Albarqouni,Amelia Jimenez Sanchez,Sonja Kirchhoff,Peter Biberthaler,Nassir Navab,Di作者: olfction 時(shí)間: 2025-3-28 19:10 作者: 松緊帶 時(shí)間: 2025-3-29 00:13 作者: Cardioplegia 時(shí)間: 2025-3-29 05:11
Yuru Pei,Yunai Yi,Gengyu Ma,Yuke Guo,Gui Chen,Tianmin Xu,Hongbin Zha作者: 圍裙 時(shí)間: 2025-3-29 08:10 作者: 蘑菇 時(shí)間: 2025-3-29 14:26
Motion Corruption Detection in Breast DCE-MRI,els; one based on a feature extraction method and a second one using a deep learning approach. These models are trained using estimates of deformation generated from unlabeled clinical data. We validate the predictions on a labeled dataset from radiologists denoting cases suffering from motion artif作者: hemophilia 時(shí)間: 2025-3-29 19:19
Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Ca for risk class assignment. The intraclass correlation coefficient is 0.98 for the left anterior descending artery (LAD), 0.88 for the left circumflex artery (LCX), and 0.98 for the right coronary artery (RCA). The implemented system offers state-of-the-art accuracy with a processing time (< 30?s) b作者: Keratectomy 時(shí)間: 2025-3-29 20:52
Atlas of Classifiers for Brain MRI Segmentation,on is independent of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities..The proposed method has been applied to publicly available datasets for the segmentation of brain MRI tissues an作者: pester 時(shí)間: 2025-3-30 02:48
Dictionary Learning and Sparse Coding-Based Denoising for High-Resolution Task Functional Connectivcientifically meaningful within motor area. The promising results show that the proposed method can provide an important foundation for the high-resolution functional connectivity analysis, and provide a better approach for fMRI preprocessing.作者: 殘暴 時(shí)間: 2025-3-30 06:32 作者: Incorporate 時(shí)間: 2025-3-30 08:34 作者: 音樂會 時(shí)間: 2025-3-30 14:23
STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion,ction network, our approach can produce high-resolution spatio-temporal volumes. The experiment results demonstrate the capability of STAR to maintain the image quality and accuracy of cerebral hemodynamic parameters at only one-third of the original scanning time.作者: placebo 時(shí)間: 2025-3-30 17:07
,Classification of Alzheimer’s Disease by Cascaded Convolutional Neural Networks Using PET Images, features. Then, a deep 3D CNNs is learned to ensemble the high-level features for final classification. The proposed method can automatically learn the generic features from PET imaging data for classification. No image segmentation and rigid registration are required in preprocessing the PET image作者: Root494 時(shí)間: 2025-3-30 23:52
Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images,nd serves as guidance for an optimal selection of tree structures. The proposed method has been tested on the label propagation of clinically-captured CBCT images. Experiments demonstrate the proposed method yields performance improvements over variants of both supervised and unsupervised random-for作者: Jargon 時(shí)間: 2025-3-31 01:48 作者: Certainty 時(shí)間: 2025-3-31 07:45
Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementicombination of modalities, via effective training using .. Specifically, in the first stage, we learn latent representations (i.e., high-level features) for each modality independently, so that the heterogeneity between modalities can be better addressed and then combined in the next stage. In the s作者: 系列 時(shí)間: 2025-3-31 12:33 作者: PHON 時(shí)間: 2025-3-31 14:29
Efficient Groupwise Registration for Brain MRI by Fast Initialization,of deformation fields, as well as their respective simulated samples using different parameters for PCA. In the application stage, when given a new set of testing brain MR images, we can mix them with the augmented training samples. Then, for each testing image, we can find its closest sample in the作者: 盡忠 時(shí)間: 2025-3-31 20:43
Sparse Multi-view Task-Centralized Learning for ASD Diagnosis,ntralized strategy for a highly efficient solution. The comprehensive experiments on the ABIDE database demonstrate that our proposed Sparse-MVTC method can significantly outperform the existing classification methods in ASD diagnosis.作者: 不利 時(shí)間: 2025-3-31 22:26 作者: Folklore 時(shí)間: 2025-4-1 01:51
Jun Wang,Qian Wang,Shitong Wang,Dinggang Shene serve the cell or does the cell serve the genome? What is the value of life and death? Can we become immortal? The inquisitive reader willfind original answers to these and other exciting questions in the pages of this stimulating book..978-3-031-27554-8978-3-031-27552-4Series ISSN 1612-3018 Series E-ISSN 2197-6619 作者: Bernstein-test 時(shí)間: 2025-4-1 09:21 作者: URN 時(shí)間: 2025-4-1 13:36 作者: demote 時(shí)間: 2025-4-1 16:03
Machine Learning in Medical Imaging978-3-319-67389-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: FRAUD 時(shí)間: 2025-4-1 19:42
