標(biāo)題: Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018; 21st International C Alejandro F. Frangi,Julia A. Schnabel,Gabor [打印本頁(yè)] 作者: firearm 時(shí)間: 2025-3-21 17:48
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018影響因子(影響力)
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018影響因子(影響力)學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018網(wǎng)絡(luò)公開度
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018被引頻次
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018被引頻次學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018年度引用
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018年度引用學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018讀者反饋
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2018讀者反饋學(xué)科排名
作者: manifestation 時(shí)間: 2025-3-21 22:24 作者: ascetic 時(shí)間: 2025-3-22 03:43
Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear segmentations of intra-cochlear anatomical structures, which are obtained with a previously published method, in the real pre-implantation and the artifact-corrected CTs. We show that the proposed method leads to an average surface error of 0.18?mm which is about half of what could be achieved with作者: 議程 時(shí)間: 2025-3-22 05:56
Deep Convolutional Filtering for Spatio-Temporal Denoising and Artifact Removal in Arterial Spin Labn of perfusion, even in challenging datasets, this technique offers an exciting new approach for ASL pipelines, and might be used both for improving individual images and to increase the power of research studies using ASL.作者: 殘酷的地方 時(shí)間: 2025-3-22 11:29
DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual L mapping from noisy perfusion-weighted image and its subtraction (residual) from the clean image. Additionally, we incorporate the CBF estimation model in the loss function during training, which enables the network to produce high quality images while simultaneously enforcing the CBF estimates to b作者: Indict 時(shí)間: 2025-3-22 15:30
Direct Estimation of Pharmacokinetic Parameters from DCE-MRI Using Deep CNN with Forward Physical Mon of PK parameters. Experiments on clinical brain DCE datasets demonstrate the efficacy of our approach in terms of fidelity of PK parameter reconstruction and significantly faster parameter inference compared to a model-based iterative reconstruction method.作者: 不在灌木叢中 時(shí)間: 2025-3-22 17:46 作者: thalamus 時(shí)間: 2025-3-23 00:01 作者: organic-matrix 時(shí)間: 2025-3-23 05:26 作者: Blanch 時(shí)間: 2025-3-23 06:45
Evaluation of Adjoint Methods in Photoacoustic Tomography with Under-Sampled Sensors BP have better performance on contrast and resolution, respectively. We also show that the integrand of TR includes additional side lobes which degrade axial resolution whereas that of BP conversely has relatively small amplitudes. Moreover, omnidirectional resolution is improved if more sensors ar作者: 糾纏 時(shí)間: 2025-3-23 10:57
A No-Reference Quality Metric for Retinal Vessel Tree Segmentationfulness of our approach by applying it to automatically find a threshold for soft probabilistic segmentations on a per-image basis. For an independent state-of-the-art unsupervised vessel segmentation technique, the thresholds selected by our approach lead to statistically significant improvements i作者: 兩棲動(dòng)物 時(shí)間: 2025-3-23 17:39
Efficient and Accurate MRI Super-Resolution Using a Generative Adversarial Network and 3D Multi-leveromotes realistic output hardly distinguishable from original HR images. Our results from experiments on a dataset with 1,113 subjects shows that our new architecture outperforms other popular deep learning methods in recovering 4x resolution-downgraded images and runs 6x faster.作者: pus840 時(shí)間: 2025-3-23 19:41
A Deep Learning Based Anti-aliasing Self Super-Resolution Algorithm for MRIsuch training data are often unavailable. This paper presents an anti-aliasing?(AA) and self super-resolution?(SSR) algorithm that needs no external training data. It takes advantage of the fact that the in-plane slices of those MR images contain high frequency information. Our algorithm consists of作者: 售穴 時(shí)間: 2025-3-23 22:59 作者: Mangle 時(shí)間: 2025-3-24 05:12
Phase-Sensitive Region-of-Interest Computed Tomography, and shows high-quality results on simulated data and on a biological mouse sample. This work is a proof of concept showing the potential to use PCI in CT on large specimen, such as humans, in clinical applications.作者: 減去 時(shí)間: 2025-3-24 09:24 作者: GRACE 時(shí)間: 2025-3-24 11:59 作者: 的’ 時(shí)間: 2025-3-24 16:13
ür Studien zu Informalit?ten hervor und liefert Anregungen für zukünftige Forschungen in der Organisations- und Rechtssoziologie.978-3-658-06154-8978-3-658-06155-5Series ISSN 2570-334X Series E-ISSN 2570-3358 作者: Detonate 時(shí)間: 2025-3-24 20:36 作者: Robust 時(shí)間: 2025-3-25 00:48 作者: EXPEL 時(shí)間: 2025-3-25 06:36
Prabhjot Kaur,Anil Kumar Saoungen vorgebeugt und die elterliche Zufriedenheit maximiert werden..?Ich freue mich, dass weitere wesentliche Lebensbereiche optimiert werden und wünsche dem Optimierungsnachwuchsbuch ein gutes Gedeihen.“.Prof. Dr. Bernd Stauss, Autor von "Optmimiert Weihnachten".978-3-658-07161-5作者: 消毒 時(shí)間: 2025-3-25 11:23
0302-9743 I: .Diffusion Tensor Imaging and Functional MRI:. Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. .Neuroimaging and Brai978-3-030-00927-4978-3-030-00928-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Rejuvenate 時(shí)間: 2025-3-25 15:06 作者: 無(wú)能力之人 時(shí)間: 2025-3-25 15:59
David Owen,Andrew Melbourne,Zach Eaton-Rosen,David L. Thomas,Neil Marlow,Jonathan Rohrer,Sébastien O作者: calamity 時(shí)間: 2025-3-25 21:13
Cagdas Ulas,Giles Tetteh,Stephan Kaczmarz,Christine Preibisch,Bjoern H. Menze作者: Spangle 時(shí)間: 2025-3-26 03:39
Cagdas Ulas,Giles Tetteh,Michael J. Thrippleton,Paul A. Armitage,Stephen D. Makin,Joanna M. Wardlaw,作者: limber 時(shí)間: 2025-3-26 05:15 作者: 提名的名單 時(shí)間: 2025-3-26 10:56
Amir Fazlollahi,Scott Ayton,Pierrick Bourgeat,Ibrahima Diouf,Parnesh Raniga,Jurgen Fripp,James Doeck作者: Favorable 時(shí)間: 2025-3-26 13:39
Hongxiang Lin,Takashi Azuma,Mehmet Burcin Unlu,Shu Takagi作者: integrated 時(shí)間: 2025-3-26 19:38
Adrian Galdran,Pedro Costa,Alessandro Bria,Teresa Araújo,Ana Maria Mendon?a,Aurélio Campilho作者: Essential 時(shí)間: 2025-3-27 00:44 作者: 物質(zhì) 時(shí)間: 2025-3-27 03:39
Stefano B. Blumberg,Ryutaro Tanno,Iasonas Kokkinos,Daniel C. Alexander作者: 創(chuàng)新 時(shí)間: 2025-3-27 06:34 作者: Metastasis 時(shí)間: 2025-3-27 12:16 作者: RENIN 時(shí)間: 2025-3-27 16:39
Yixing Huang,Tobias Würfl,Katharina Breininger,Ling Liu,Günter Lauritsch,Andreas Maier作者: 提煉 時(shí)間: 2025-3-27 18:21
Conference proceedings 2018mputing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018...The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: .P作者: 被詛咒的人 時(shí)間: 2025-3-28 01:16
0302-9743 l Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018...The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical se作者: Bereavement 時(shí)間: 2025-3-28 05:24 作者: Default 時(shí)間: 2025-3-28 09:34
Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Imagescuracy and show that deeper models have more predictive power, which may exploit larger training sets. We obtain substantially better results than the previous state-of-the-art model with a slight memory increase, reducing the root-mean-squared-error by 13%. Our code is publicly available.作者: FLOAT 時(shí)間: 2025-3-28 11:46 作者: 離開真充足 時(shí)間: 2025-3-28 14:55
Includes supplementary material: ?Es handelt sich bei diesem Band um eine Fallstudie, die in den überschneidungsbereich der Organisations- und Risikosoziologie geh?rt. Exemplarisch wird an dem Schiffsunglück des Kreuzfahrtschiffs Costa Concordia ein Zusammenhang zwischen beiden Forschungsgebieten he作者: 品牌 時(shí)間: 2025-3-28 19:33
Jianing Wang,Yiyuan Zhao,Jack H. Noble,Benoit M. DawantIncludes supplementary material: ?Es handelt sich bei diesem Band um eine Fallstudie, die in den überschneidungsbereich der Organisations- und Risikosoziologie geh?rt. Exemplarisch wird an dem Schiffsunglück des Kreuzfahrtschiffs Costa Concordia ein Zusammenhang zwischen beiden Forschungsgebieten he作者: 野蠻 時(shí)間: 2025-3-29 01:10 作者: Subjugate 時(shí)間: 2025-3-29 05:12 作者: 水獺 時(shí)間: 2025-3-29 10:05
Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear(CT) ear images of cochlear implants (CIs) recipients. Our training set contains paired pre-implantation and post-implantation CTs of 90 ears. At the training phase, the cGAN learns a mapping from the artifact-affected CTs to the artifact-free CTs. At the inference phase, given new metal-artifact-af作者: 撫育 時(shí)間: 2025-3-29 11:52 作者: 絕緣 時(shí)間: 2025-3-29 16:19
Deep Convolutional Filtering for Spatio-Temporal Denoising and Artifact Removal in Arterial Spin Labsion in the brain and other organs. However, because the signal-to-noise ratio is inherently low and the ASL acquisition is particularly prone to corruption by artifact, image processing methods such as denoising and artifact filtering are vital for generating accurate measurements of perfusion. In 作者: 煩擾 時(shí)間: 2025-3-29 20:55 作者: 裁決 時(shí)間: 2025-3-30 03:15 作者: ethnology 時(shí)間: 2025-3-30 05:30 作者: 乳白光 時(shí)間: 2025-3-30 08:21
Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?e been made to develop miniaturized . imaging devices, specifically confocal laser microscopes, for both clinical and research applications. However, current implementations of miniature CLE components such as confocal lenses compromise image resolution, signal-to-noise ratio, or both, which negativ作者: Latency 時(shí)間: 2025-3-30 14:01 作者: 大方不好 時(shí)間: 2025-3-30 20:19 作者: notion 時(shí)間: 2025-3-31 00:37
A No-Reference Quality Metric for Retinal Vessel Tree Segmentationios, the ability to automatically assess the quality of predictions when no expert annotation is available can be critical. In this paper, we propose a new method for quality assessment of retinal vessel tree segmentations in the absence of a reference ground-truth. For this, we artificially degrade作者: cringe 時(shí)間: 2025-3-31 00:55 作者: Pillory 時(shí)間: 2025-3-31 06:37
A Deep Learning Based Anti-aliasing Self Super-Resolution Algorithm for MRIio requires a long time, making them costly and susceptible to motion artifacts. A common way to partly achieve this goal is to acquire MR images with good in-plane resolution and poor through-plane resolution?(i.e., large slice thickness). For such 2D imaging protocols, aliasing is also introduced 作者: 怕失去錢 時(shí)間: 2025-3-31 10:54 作者: 臆斷 時(shí)間: 2025-3-31 14:28
Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Imagesarning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network’s depth, to being roughly constant – permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the p作者: neutralize 時(shí)間: 2025-3-31 18:04 作者: gratify 時(shí)間: 2025-4-1 00:12 作者: Anonymous 時(shí)間: 2025-4-1 04:52
Some Investigations on Robustness of Deep Learning in Limited Angle Tomographydata, reconstructed images suffer from artifacts, which cause boundary distortion, edge blurring, and intensity biases. Recently, deep learning methods have been applied very successfully to this problem in simulation studies. However, the robustness of neural networks for clinical applications is s作者: 難聽的聲音 時(shí)間: 2025-4-1 07:23
978-3-030-00927-4Springer Nature Switzerland AG 2018作者: 外露 時(shí)間: 2025-4-1 13:20 作者: Vertebra 時(shí)間: 2025-4-1 17:43
https://doi.org/10.1007/978-3-030-00928-1Artificial intelligence; Classification; Computer vision; Estimation; Image analysis; Image enhancement; I作者: MIR 時(shí)間: 2025-4-1 21:24
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/629189.jpg
