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標(biāo)題: Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019; 22nd International C Dinggang Shen,Tianming Liu,Ali Khan Conferen [打印本頁]

作者: supplementary 時間: 2025-3-21 17:35
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019影響因子(影響力)

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019影響因子(影響力)學(xué)科排名

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019網(wǎng)絡(luò)公開度

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019網(wǎng)絡(luò)公開度學(xué)科排名

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019被引頻次

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019被引頻次學(xué)科排名

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019年度引用

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019年度引用學(xué)科排名

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019讀者反饋

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2019讀者反饋學(xué)科排名

作者: 向外才掩飾 時間: 2025-3-21 23:36

作者: 我不怕犧牲 時間: 2025-3-22 03:24
Yu Zhao,Yuan Liu,Yansheng Kan,Anjany Sekuboyina,Diana Waldmannstetter,Hongwei Li,Xiaobin Hu,Xiaozhi Organisationsforschung.Includes supplementary material: .Zweigrundlegende Perspektiven organisationsp?dagogischer Theoriebildung bilden denGegenstand des Bandes: solche, die organisationsp?dagogische Fragestellungenmittels Rückgriff auf Theorien anderer disziplin?rer Herkunft (Soziologie,Management

作者: 緩解 時間: 2025-3-22 05:02

作者: 分發(fā) 時間: 2025-3-22 12:15
Siyuan Pan,Xuhong Hou,Huating Li,Bin Sheng,Ruogu Fang,Yuxin Xue,Weiping Jia,Jing Qin Organisationsforschung.Includes supplementary material: .Zweigrundlegende Perspektiven organisationsp?dagogischer Theoriebildung bilden denGegenstand des Bandes: solche, die organisationsp?dagogische Fragestellungenmittels Rückgriff auf Theorien anderer disziplin?rer Herkunft (Soziologie,Management

作者: Anticoagulant 時間: 2025-3-22 14:10
Mattias P. Heinrich Organisationsforschung.Includes supplementary material: .Zweigrundlegende Perspektiven organisationsp?dagogischer Theoriebildung bilden denGegenstand des Bandes: solche, die organisationsp?dagogische Fragestellungenmittels Rückgriff auf Theorien anderer disziplin?rer Herkunft (Soziologie,Management

作者: 磨碎 時間: 2025-3-22 20:14
Hristina Uzunova,Jan Ehrhardt,Fabian Jacob,Alex Frydrychowicz,Heinz Handelsleichsam als ?Tr?ger“ dieser Relation. Dabei wird stillschweigend vorausgesetzt, dass auf der einen Seite das Arbeitsverm?gen und auf der anderen Seite die Stellen nach einem bestimmten Schema typisiert sind. Um genau diese Voraussetzung geht es im Beitrag. Es werden die Mechanismen und Schemata der

作者: SOBER 時間: 2025-3-22 22:08

作者: 信任 時間: 2025-3-23 03:44
Mohammad Arafat Hussain,Ghassan Hamarneh,Rafeef Garbiven Bildungsforschung.Includes supplementary material: Der Band beleuchtet das Verh?ltnis von Organisation und Bildung in theoretischen und empirischen Analysen. Das zentrale Anliegen ist es, zu zeigen, wie unterschiedliche organisationssoziologische Ans?tze für die qualitative Bildungsforschung fru

作者: 武器 時間: 2025-3-23 09:29
Yuting He,Guanyu Yang,Yang Chen,Youyong Kong,Jiasong Wu,Lijun Tang,Xiaomei Zhu,Jean-Louis Dillensegeleichsam als ?Tr?ger“ dieser Relation. Dabei wird stillschweigend vorausgesetzt, dass auf der einen Seite das Arbeitsverm?gen und auf der anderen Seite die Stellen nach einem bestimmten Schema typisiert sind. Um genau diese Voraussetzung geht es im Beitrag. Es werden die Mechanismen und Schemata der

作者: 制定 時間: 2025-3-23 12:30
Han Zheng,Lanfen Lin,Hongjie Hu,Qiaowei Zhang,Qingqing Chen,Yutaro Iwamoto,Xianhua Han,Yen-Wei Chen, Informationssuche, effizienterem Online-Zeitmanagement und wirkungsvollerem Networking. Wer als Manager nicht von den ?Digital Natives“ abgeh?ngt werden will, ben?tigt profundes Wissen über die verschiedenen Online-Instrumente und deren Einsatz. Doch konkrete Anleitungen gibt es weder für Google no

作者: Detoxification 時間: 2025-3-23 15:22
Renzhen Wang,Shilei Cao,Kai Ma,Deyu Meng,Yefeng Zheng Informationssuche, effizienterem Online-Zeitmanagement und wirkungsvollerem Networking. Wer als Manager nicht von den ?Digital Natives“ abgeh?ngt werden will, ben?tigt profundes Wissen über die verschiedenen Online-Instrumente und deren Einsatz. Doch konkrete Anleitungen gibt es weder für Google no

作者: 有特色 時間: 2025-3-23 20:14

作者: 傳授知識 時間: 2025-3-24 01:49
MVP-Net: Multi-view FPN with Position-Aware Attention for Deep Universal Lesion Detectiones have been proposed for ULD, aiming to learn representative features from annotated CT data. However, the hunger for data of deep learning models and the scarcity of medical annotation hinders these approaches to advance further. In this paper, we propose to incorporate domain knowledge in clinica

作者: 臆斷 時間: 2025-3-24 05:34
Spatial-Frequency Non-local Convolutional LSTM Network for pRCC Classificationtures when the data size is small and the data dimension is large. To solve this problem, we develop a spatial-frequency non-local convolutional LSTM network for 3D image classification. Compared to traditional networks, the proposed model has the ability to extract features from both the spatial an

作者: 匍匐前進(jìn) 時間: 2025-3-24 07:49

作者: galley 時間: 2025-3-24 10:53

作者: Serenity 時間: 2025-3-24 18:11
Closing the Gap Between Deep and Conventional Image Registration Using Probabilistic Dense Displacemotherapy as well as motion analysis all rely heavily on accurate intra-patient alignment. Furthermore, inter-patient registration enables atlas-based segmentation or landmark localisation and shape analysis. When labelled scans are scarce and anatomical differences large, conventional registration h

作者: lipids 時間: 2025-3-24 21:01

作者: Creditee 時間: 2025-3-25 02:53
PAN: Projective Adversarial Network for Medical Image Segmentationedical imaging, capturing 3D semantics in an effective yet computationally efficient way remains an open problem. In this study, we address this computational burden by proposing a novel projective adversarial network, called PAN, which incorporates high-level 3D information through 2D projections.

作者: 惰性氣體 時間: 2025-3-25 06:37
Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram metal trace with synthesized data. However, existing projection or sinogram completion methods cannot always produce anatomically consistent information to fill the metal trace, and thus, when the metallic implant is large, significant secondary artifacts are often introduced. In this work, we prop

作者: 大漩渦 時間: 2025-3-25 08:46
Multi-class Gradient Harmonized Dice Loss with Application to Knee MR Image Segmentationund and background voxels. However, it is not able to differentiate hard examples from easy ones, which usually comprise the majority of training examples and therefore dominate the loss function. In this work, we propose a novel loss function, termed as ., to both address the quantity imbalance bet

作者: Sleep-Paralysis 時間: 2025-3-25 14:45
LSRC: A Long-Short Range Context-Fusing Framework for Automatic 3D Vertebra Localizationcomplex pathological conditions, and limited field-of-view in 3D CT images. The local and long-range contextual information is especially useful for solving this problem. To explore both the local and long-range contextual information of vertebrae, in this paper, we propose a new framework called .o

作者: nugatory 時間: 2025-3-25 16:22

作者: Cantankerous 時間: 2025-3-25 23:06

作者: 貧窮地活 時間: 2025-3-26 02:44
Deep Learning Based Metal Artifacts Reduction in Post-operative Cochlear Implant CT Imaging the cochlea based on post-operative CT imaging. Yet, these images suffer from metal artifacts which often entail a difficulty to make any analysis. In this work, we propose a 3D metal artifact reduction method using convolutional neural networks for post-operative cochlear implant imaging. Our appr

作者: 曲解 時間: 2025-3-26 06:42
ImHistNet: Learnable Image Histogram Based DNN with Application to Noninvasive Determination of CarcRCC) is the major subtype of RCC and its biological aggressiveness affects prognosis and treatment planning. An important ccRCC prognostic predictor is its ‘grade’ for which the 4-tiered Fuhrman grading system is used. Although the Fuhrman grade can be identified by percutaneous renal biopsy, recent

作者: Pamphlet 時間: 2025-3-26 09:19
DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Dete the interlobar artery’s corresponding blood feeding region easily. However, it is still a challenging task that no one has reported success due to the large intra-scale changes, large inter-anatomy variation, thin structures, small volume ratio and limitation of labeled data. Hence, in this paper

作者: Prologue 時間: 2025-3-26 13:33
Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Priormethods have achieved great success in computer vision domain, there are still several challenges in medical image domain. In comparison with natural images, medical image databases are usually small because the annotation is extremely time-consuming and requires expert knowledge. Thus, effective us

作者: NUL 時間: 2025-3-26 18:02
Pairwise Semantic Segmentation via Conjugate Fully Convolutional Networkd large enough. However, FCNs often fail to achieve satisfactory results due to a limited number of manually labelled samples in medical imaging. In this paper, we propose a conjugate fully convolutional network (CFCN) to address this challenging problem. CFCN is a novel framework where pairwise sam

作者: Coeval 時間: 2025-3-26 22:16

作者: 短程旅游 時間: 2025-3-27 05:11

作者: chisel 時間: 2025-3-27 05:50

作者: 厚臉皮 時間: 2025-3-27 10:07
Multi-class Gradient Harmonized Dice Loss with Application to Knee MR Image Segmentation knee scans collected from local hospitals. The experimental results show that the Gradient Harmonized Dice Loss outperforms the popular loss functions, such as Dice loss and Focal loss, and achieves the state-of-the-art results on the validation data of ..

作者: chapel 時間: 2025-3-27 17:02

作者: 身體萌芽 時間: 2025-3-27 20:59
0302-9743 nce on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019...The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topi

作者: 假裝是我 時間: 2025-3-27 23:31
PAN: Projective Adversarial Network for Medical Image Segmentationur adversarial network. For the clinical application we chose pancreas segmentation from CT scans. Our proposed framework achieved state-of-the-art performance without adding to the complexity of the segmentor.

作者: 暫時休息 時間: 2025-3-28 02:53
Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Imagesd of our method, arbitrarily large images of high resolution can be generated. Moreover, compared to common patch-based approaches, our multi-resolution scheme enables better image quality and prevents patch artifacts.

作者: 審問 時間: 2025-3-28 08:48
Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network and shape changes in a small number of training samples, a fusion module is designed to provide proxy supervision for the network training process. Quantitative evaluation shows that the proposed method has a significant performance improvement on pathological liver segmentation.

作者: Agility 時間: 2025-3-28 13:37

作者: figment 時間: 2025-3-28 16:46

作者: Lipoprotein 時間: 2025-3-28 20:29
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/629191.jpg

作者: annexation 時間: 2025-3-28 23:51

作者: gastritis 時間: 2025-3-29 03:07

作者: 單獨 時間: 2025-3-29 10:13

作者: 愛國者 時間: 2025-3-29 13:15

作者: ESO 時間: 2025-3-29 16:06
Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning9000 clinical quantitative MR images with numerical labels of the number of pixels in subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and Non-adipose tissues. Our approach achieves 94.3% and 90.8% segmentation accuracy for SAT and VAT respectively in the dataset with image labels,

作者: 彎彎曲曲 時間: 2025-3-29 20:15

作者: 滋養(yǎng) 時間: 2025-3-30 00:43
Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning10 test patients and 60 training/validation patients. We then trained a hierarchically densely connected convolutional U-net (HD U-net), to take the PTV and avoidance map representing OARs masks and weights, and predict the optimized plan. The HD U-net is capable of accurately predicting the 3D Pare

作者: Ganglion-Cyst 時間: 2025-3-30 07:50
LSRC: A Long-Short Range Context-Fusing Framework for Automatic 3D Vertebra Localizationgrates the long-range contextual information of these two views. Two refined heat maps in the sagittal and coronal planes are generated by a globally-refining module, adjusting vertebra locations using the global location information in an attention manner. Experiments on a public dataset of 302 3D

作者: Cytokines 時間: 2025-3-30 08:22

作者: jocular 時間: 2025-3-30 16:08

作者: CODE 時間: 2025-3-30 18:25
DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and DeDPA) for semi-supervised learning of thin structures. Differ from other semi-supervised methods, it embeds priori anatomical features to segmentation network which avoids inaccurate results sensitive to thin structures as optimizing targets, so that the network achieves generalization of different a

作者: misanthrope 時間: 2025-3-30 23:39

作者: Immunoglobulin 時間: 2025-3-31 04:14

作者: 雇傭兵 時間: 2025-3-31 05:01
Conference proceedings 2019ional neuroimaging (fMRI); miscellaneous neuroimaging..Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis..Part V: computer assisted interventions; MIC meets CAI.. Part VI: computed tomography; X-ray imaging..

作者: overrule 時間: 2025-3-31 09:48