標(biāo)題: Titlebook: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016; 19th International C Sebastien Ourselin,Leo Joskowicz,William Wel [打印本頁(yè)] 作者: counterfeit 時(shí)間: 2025-3-21 16:22
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016影響因子(影響力)
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016影響因子(影響力)學(xué)科排名
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016被引頻次
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016被引頻次學(xué)科排名
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016年度引用
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016年度引用學(xué)科排名
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016讀者反饋
書(shū)目名稱Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016讀者反饋學(xué)科排名
作者: BARGE 時(shí)間: 2025-3-21 23:26 作者: 本土 時(shí)間: 2025-3-22 02:32 作者: aerial 時(shí)間: 2025-3-22 07:11 作者: Veneer 時(shí)間: 2025-3-22 11:35 作者: affluent 時(shí)間: 2025-3-22 13:26 作者: 冒煙 時(shí)間: 2025-3-22 17:51 作者: Cholagogue 時(shí)間: 2025-3-22 22:10 作者: Thymus 時(shí)間: 2025-3-23 03:15 作者: 清唱?jiǎng)?nbsp; 時(shí)間: 2025-3-23 05:47 作者: Cupidity 時(shí)間: 2025-3-23 12:21 作者: Affection 時(shí)間: 2025-3-23 13:54 作者: Commonplace 時(shí)間: 2025-3-23 21:35 作者: 堅(jiān)毅 時(shí)間: 2025-3-24 00:33
Multimodal Deep Learning for Cervical Dysplasia Diagnosis, However, current multimodal frameworks suffer from low sensitivity at high specificity levels, due to their limitations in learning correlations among highly heterogeneous modalities. In this paper, we design a deep learning framework for cervical dysplasia diagnosis by leveraging multimodal inform作者: 前兆 時(shí)間: 2025-3-24 06:05 作者: 人類的發(fā)源 時(shí)間: 2025-3-24 06:50 作者: 撫育 時(shí)間: 2025-3-24 11:41
Deep Retinal Image Understanding, disc segmentation. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in other fields of computer vision such as object detection and image classification, and we bring their power to the study of eye fundus images. DRIU uses a base network architecture on whi作者: 使痛苦 時(shí)間: 2025-3-24 15:52 作者: enchant 時(shí)間: 2025-3-24 21:17
Deep Neural Networks for Fast Segmentation of 3D Medical Images, image classification and semantic segmentation. In this paper, a CNN for 3D volume segmentation based on recently introduced deep learning components will be presented. In addition to using image patches as input for a CNN, the usage of orthogonal patches, which combine shape and locality informati作者: Individual 時(shí)間: 2025-3-25 03:01 作者: inundate 時(shí)間: 2025-3-25 04:31
Neeraj Dhungel,Gustavo Carneiro,Andrew P. Bradleynd Learning (CRYSTAL) funded for 5 years (2005–2010) by the Natural Sciences and Engineering Research Council Canada (NSERC). Pacific CRYSTAL intended to promote scientific, mathematical, and technological literacy for responsible citizenship through research partnerships with university and educati作者: 畏縮 時(shí)間: 2025-3-25 09:12
Tao Xu,Han Zhang,Xiaolei Huang,Shaoting Zhang,Dimitris N. Metaxasl Literacy is one of five Centres for Research into Youth, Science Teaching and Learning (CRYSTAL) funded for 5 years (2005–2010) by the Natural Sciences and Engineering Research Council Canada (NSERC). Pacific CRYSTAL intended to promote scientific, mathematical, and technological literacy for resp作者: 頭盔 時(shí)間: 2025-3-25 15:16
Wei Shen,Mu Zhou,Feng Yang,Di Dong,Caiyun Yang,Yali Zang,Jie Tiannd Learning (CRYSTAL) funded for 5 years (2005–2010) by the Natural Sciences and Engineering Research Council Canada (NSERC). Pacific CRYSTAL intended to promote scientific, mathematical, and technological literacy for responsible citizenship through research partnerships with university and educati作者: amygdala 時(shí)間: 2025-3-25 16:07 作者: 混沌 時(shí)間: 2025-3-25 21:27 作者: 禁令 時(shí)間: 2025-3-26 01:29
Qi Dou,Hao Chen,Yueming Jin,Lequan Yu,Jing Qin,Pheng-Ann Hengnd Learning (CRYSTAL) funded for 5 years (2005–2010) by the Natural Sciences and Engineering Research Council Canada (NSERC). Pacific CRYSTAL intended to promote scientific, mathematical, and technological literacy for responsible citizenship through research partnerships with university and educati作者: EVICT 時(shí)間: 2025-3-26 06:54 作者: goodwill 時(shí)間: 2025-3-26 11:34
Stability-Weighted Matrix Completion of Incomplete Multi-modal Data for Disease Diagnosis,learning to obtain a stable set of weights. Incorporating these weights into LRMC, swLRMC can better account for differences in features and modalities for improving diagnosis. Experimental results confirm that the proposed method outperforms the conventional LRMC, feature-selection based LRMC, and other state-of-the-art methods.作者: 帽子 時(shí)間: 2025-3-26 15:06 作者: 炸壞 時(shí)間: 2025-3-26 18:13 作者: 記成螞蟻 時(shí)間: 2025-3-26 23:19 作者: 捏造 時(shí)間: 2025-3-27 02:01
0302-9743 al Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers ha作者: licence 時(shí)間: 2025-3-27 06:10
Deep Retinal Image Understanding,alitative and quantitative, in four public datasets for these tasks. In all of them, DRIU presents super-human performance, that is, it shows results more consistent with a gold standard than a second human annotator used as control.作者: 閑蕩 時(shí)間: 2025-3-27 11:12 作者: LUT 時(shí)間: 2025-3-27 16:19
https://doi.org/10.1007/978-3-319-46723-8biomechanical simulation; brain network analysis; computer aided diagnosis; robot intervention; surgical作者: hypertension 時(shí)間: 2025-3-27 19:12
Sebastien Ourselin,Leo Joskowicz,William WellsIncludes supplementary material: 作者: 拋射物 時(shí)間: 2025-3-28 01:47 作者: 忘恩負(fù)義的人 時(shí)間: 2025-3-28 05:08 作者: GOAT 時(shí)間: 2025-3-28 07:52 作者: Spangle 時(shí)間: 2025-3-28 12:17 作者: DRILL 時(shí)間: 2025-3-28 15:32 作者: 母豬 時(shí)間: 2025-3-28 19:35 作者: narcissism 時(shí)間: 2025-3-28 22:54 作者: Ischemia 時(shí)間: 2025-3-29 04:35
Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networksonance imaging (rs-fMRI)). Therefore, integrating connectomics information in outcome prediction could improve prediction accuracy. To this end, we unprecedentedly devise a machine learning-based HGG prediction framework that can effectively extract valuable features from complex human brain connec作者: 拋媚眼 時(shí)間: 2025-3-29 09:06
Mammographic Mass Segmentation with Online Learned Shape and Appearance Priors,proach is extensively validated on a large dataset constructed on DDSM. Results demonstrate that our online learned priors lead to substantial improvement in mass segmentation accuracy, compared with previous systems.作者: 增強(qiáng) 時(shí)間: 2025-3-29 11:32 作者: anchor 時(shí)間: 2025-3-29 18:05 作者: 幼稚 時(shí)間: 2025-3-29 21:47
Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PEe proposed method aims to reduce the imprecision and overlaps between different classes in the selected feature subspace, thus finally improving the prediction accuracy. It has been evaluated by two clinical datasets, showing good performance.作者: 描述 時(shí)間: 2025-3-30 01:08
,Structured Sparse Kernel Learning for Imaging Genetics Based Alzheimer’s Disease Diagnosis,lzheimer’s Disease Neuroimaging Initiative (ADNI) database. The effectiveness of our method is demonstrated by both the clearly improved prediction accuracy and the discovered brain regions and SNPs relevant to AD.作者: 協(xié)奏曲 時(shí)間: 2025-3-30 05:37
,Semi-supervised Hierarchical Multimodal Feature and Sample Selection for Alzheimer’s Disease Diagno we utilize both labeled and unlabeled data in the learning process, making our method semi-supervised. The finally selected features and samples are then used to train support vector machine (SVM) based classification models. Our method is evaluated on 702 subjects from the Alzheimer’s Disease Neur作者: exclusice 時(shí)間: 2025-3-30 09:46 作者: Fabric 時(shí)間: 2025-3-30 15:52
The Automated Learning of Deep Features for Breast Mass Classification from Mammograms,cation results, compared with the machine learning model using hand-crafted features and with deep learning method trained directly for the classification stage without the pre-training stage. We also show that the proposed method produces the current state-of-the-art breast mass classification resu作者: 直覺(jué)好 時(shí)間: 2025-3-30 19:08
Multimodal Deep Learning for Cervical Dysplasia Diagnosis,vical dysplasia with 87.83?% sensitivity at 90?% specificity on a large dataset, which significantly outperforms methods using any single source of information alone and previous multimodal frameworks.作者: 橫條 時(shí)間: 2025-3-30 21:02 作者: malapropism 時(shí)間: 2025-3-31 04:35
0302-9743 n and deformation estimation;? shape modeling; cardiac and vascular image analysis; image reconstruction; and MR imageanalysis.978-3-319-46722-1978-3-319-46723-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: neuron 時(shí)間: 2025-3-31 05:53 作者: Vulnerary 時(shí)間: 2025-3-31 09:43 作者: BLINK 時(shí)間: 2025-3-31 13:59
Kevis-Kokitsi Maninis,Jordi Pont-Tuset,Pablo Arbeláez,Luc Van Gooles; and Node 3, lighthouse schools, involved systemic change and leadership opportunities that adapted, demonstrated, and disseminated tested ideas, resources, and strategies to a much broader education community and attempted 978-94-6091-506-2作者: 冒煙 時(shí)間: 2025-3-31 20:54
Luyan Liu,Qian Wang,Ehsan Adeli,Lichi Zhang,Han Zhang,Dinggang Shen作者: 顯赫的人 時(shí)間: 2025-3-31 22:39
Brent C. Munsell,Guorong Wu,Yue Gao,Nicholas Desisto,Martin Styner作者: 模仿 時(shí)間: 2025-4-1 04:57 作者: Foment 時(shí)間: 2025-4-1 09:18
Menglin Jiang,Shaoting Zhang,Yuanjie Zheng,Dimitris N. Metaxas作者: 貝雷帽 時(shí)間: 2025-4-1 13:19
Christian Ledig,Sebastian Kaltwang,Antti Tolonen,Juha Koikkalainen,Philip Scheltens,Frederik Barkhof作者: Working-Memory 時(shí)間: 2025-4-1 15:39
Sihong Chen,Dong Ni,Jing Qin,Baiying Lei,Tianfu Wang,Jie-Zhi Cheng作者: narcissism 時(shí)間: 2025-4-1 19:57 作者: Toxoid-Vaccines 時(shí)間: 2025-4-1 23:33
Jailin Peng,Le An,Xiaofeng Zhu,Yan Jin,Dinggang Shen
