標(biāo)題: Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay [打印本頁] 作者: Traction 時間: 2025-3-21 16:13
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影響因子(影響力)
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影響因子(影響力)學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023網(wǎng)絡(luò)公開度
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引頻次
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引頻次學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023讀者反饋
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023讀者反饋學(xué)科排名
作者: Cerumen 時間: 2025-3-21 22:04
978-3-031-43998-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: NAUT 時間: 2025-3-22 02:54
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023978-3-031-43999-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Canvas 時間: 2025-3-22 06:25 作者: 美學(xué) 時間: 2025-3-22 11:17
Learned Alternating Minimization Algorithm for?Dual-Domain Sparse-View CT Reconstructioncture into the design of LAMA. We show that LAMA substantially reduces network complexity, improves memory efficiency and reconstruction accuracy, and is provably convergent for reliable reconstructions. Extensive numerical experiments demonstrate that LAMA outperforms existing methods by a wide margin on multiple benchmark CT datasets.作者: Cupping 時間: 2025-3-22 15:10 作者: Bravura 時間: 2025-3-22 17:05 作者: acolyte 時間: 2025-3-22 22:01 作者: 伴隨而來 時間: 2025-3-23 03:46
CDiffMR: Can We Replace the?Gaussian Noise with?K-Space Undersampling for?Fast MRI?can be reused for reconstruction tasks with different undersampling rates. We demonstrated, through extensive numerical and visual experiments, that the proposed CDiffMR can achieve comparable or even superior reconstruction results than state-of-the-art models. Compared to the diffusion model-based作者: HAUNT 時間: 2025-3-23 07:15
Learning Deep Intensity Field for?Extremely Sparse-View CBCT Reconstructionated by a fusion module for intensity estimation. Notably, thousands of points can be processed in parallel to improve efficiency during training and testing. In practice, we collect a knee CBCT dataset to train and evaluate DIF-Net. Extensive experiments show that our approach can reconstruct CBCT 作者: 高調(diào) 時間: 2025-3-23 12:01 作者: tariff 時間: 2025-3-23 16:15 作者: 經(jīng)典 時間: 2025-3-23 21:28
An Explainable Deep Framework: Towards Task-Specific Fusion for?Multi-to-One MRI Synthesisverage module; (2) highlight the area the network tried to refine during synthesizing by a task-specific attention module. We conduct experiments on the BraTS2021 dataset of 1251 subjects, and results on arbitrary sequence synthesis indicate that the proposed method achieves better performance than 作者: Tailor 時間: 2025-3-23 22:39 作者: evaculate 時間: 2025-3-24 05:27 作者: browbeat 時間: 2025-3-24 07:43 作者: Deference 時間: 2025-3-24 12:57
Noise Conditioned Weight Modulation for?Robust and?Generalizable Low Dose CT Denoisingc benchmark datasets show that the proposed dynamic convolutional layer significantly improves the denoising performance of the baseline network, as well as its robustness and generalization to previously unseen noise levels.作者: 變化 時間: 2025-3-24 16:05
Low-Dose CT Image Super-Resolution Network with?Dual-Guidance Feature Distillation and?Dual-Path ConThe DGFM guides the network to concentrate the feature representation of the 3D inter-slice information in the region of interest (ROI) by introducing the average CT image and segmentation mask as complements of the original LDCT input. Meanwhile, the elaborate SAB utilizes the essential multi-scale作者: 閑聊 時間: 2025-3-24 20:29 作者: Congeal 時間: 2025-3-25 00:00 作者: Bumptious 時間: 2025-3-25 04:19
Estimation of?3T MR Images from?1.5T Images Regularized with?Physics Based Constraintof 1.5T images and avoid the expensive requirements of example images and associated image registration. The LF and HF images are assumed to be related by a linear transformation (LT). The unknown HF image and unknown LT are estimated in alternate minimization framework. Further, a physics based con作者: GRUEL 時間: 2025-3-25 08:27 作者: Complement 時間: 2025-3-25 13:34 作者: Desert 時間: 2025-3-25 17:01 作者: 腐爛 時間: 2025-3-25 21:58
Conference proceedings 2023I: Clinical applications – abdomen; clinicalapplications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applicatio作者: 瑣碎 時間: 2025-3-26 01:06 作者: 溫順 時間: 2025-3-26 07:32 作者: 并排上下 時間: 2025-3-26 11:24
Sutanu Bera,Prabir Kumar Biswastand der Forschung wiedergibt. Es kann auch als OML-Consulting dienen, indem Entscheider und Praktiker OML anpassen und für ihre Anwendung einsetzen, um abzuw?gen, ob die Vorteile die Kosten aufwiegen..978-3-658-42505-0作者: eardrum 時間: 2025-3-26 13:17 作者: 帶來墨水 時間: 2025-3-26 19:47 作者: engender 時間: 2025-3-26 22:42 作者: fringe 時間: 2025-3-27 03:27
n anhand einer Forschungsarbeit im Sozialbereich im ersten Teil die Erwartungen und Arbeitsmotive der aktuell jüngsten Generation von Sozialarbeitenden aufgezeigt und diskutiert. Anschlie?end liegt der Fokus auf der Arbeitgeberattraktivit?t. Was unternehmen Führungsverantwortliche ganz konkret, um j作者: pacific 時間: 2025-3-27 06:08
Long Bai,Tong Chen,Yanan Wu,An Wang,Mobarakol Islam,Hongliang Renand von einschl?gigen Aufgabenstellungenund L?sungen. Das Werk gibt damit eine verst?ndliche Einführung in die Architektur von Betriebssystemen und eignet sich deshalb auch für die Lehre im Bachelorstudium.?Memory management, hardware management, process administration and interprocess communication作者: EWE 時間: 2025-3-27 09:42
Luyi Han,Tianyu Zhang,Yunzhi Huang,Haoran Dou,Xin Wang,Yuan Gao,Chunyao Lu,Tao Tan,Ritse Mannand von einschl?gigen Aufgabenstellungenund L?sungen. Das Werk gibt damit eine verst?ndliche Einführung in die Architektur von Betriebssystemen und eignet sich deshalb auch für die Lehre im Bachelorstudium.?Memory management, hardware management, process administration and interprocess communication作者: OPINE 時間: 2025-3-27 15:26
Haiyang Mao,Yanyang Wang,Hengyong Yu,Weiwen Wu,Jianjia Zhangligenz als Teil der Organisationskultur heraushebt. Um von der Konvergenz zu profitieren und damit eine verantwortungsvolle Gesellschaft zu schaffen, muss eine menschenzentrierte Sichtweise eingenommen werden. Das ist, was ?kosystem-Leadership ausmacht und ein aufbauendes normatives Ergebnis der dig作者: epinephrine 時間: 2025-3-27 20:35 作者: 原諒 時間: 2025-3-28 00:07
Weitong Zhang,Berke Basaran,Qingjie Meng,Matthew Baugh,Jonathan Stelter,Phillip Lung,Uday Patel,Wenj in dieser Studie hergeleitet und ist der Kern der Methode. Die sich daraus ergebenden ?konomischen und ?kologischen Vorteile sind im Verh?ltnis zu den sich zeigenden Kosten und Energiebedarfen signifikant..978-3-658-43112-9978-3-658-43113-6Series ISSN 2567-0042 Series E-ISSN 2567-0352 作者: rods366 時間: 2025-3-28 04:49
Chi Ding,Qingchao Zhang,Ge Wang,Xiaojing Ye,Yunmei Chenh verbleiben auch unter Berücksichtigung dieser Merkmale genuine Besonderheiten Ostdeutschlands (Kontexteffekte). Vor diesem Hintergrund bildet Ostdeutschland eine sozial relevante Lebenswelt und ein kontroverses Identit?tsangebot.?.978-3-658-43484-7978-3-658-43485-4作者: Myofibrils 時間: 2025-3-28 09:43 作者: GENUS 時間: 2025-3-28 10:36
0302-9743 nical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applicatio978-3-031-43998-8978-3-031-43999-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 有毛就脫毛 時間: 2025-3-28 16:33 作者: 疏遠(yuǎn)天際 時間: 2025-3-28 20:35
Zhiyun Song,Xin Wang,Xiangyu Zhao,Sheng Wang,Zhenrong Shen,Zixu Zhuang,Mengjun Liu,Qian Wang,Lichi Zkt der Resilienz das vorletzte Kapitel gewidmet, bevor sich ein Ausblick in die Zukunft anschlie?t.?.Die didaktische Struktur des Lehrbuchs enth?lt neben Lernzielen, Praxisbeispielen und Fallstudien sowie Lernv978-3-658-42338-4978-3-658-42339-1作者: 分貝 時間: 2025-3-29 01:28
Hadrien Reynaud,Mengyun Qiao,Mischa Dombrowski,Thomas Day,Reza Razavi,Alberto Gomez,Paul Leeson,Bernhe study area for assessing this approach is Louisiana (United States), which – being spatially quite diverse – has been intensively shaped for more than a century by the activities of the petrochemical industr978-3-658-43395-6978-3-658-43396-3Series ISSN 2625-6991 Series E-ISSN 2625-7009 作者: Aviary 時間: 2025-3-29 05:46
Yongsheng Pan,Feihong Liu,Caiwen Jiang,Jiawei Huang,Yong Xia,Dinggang Shen作者: enmesh 時間: 2025-3-29 07:31 作者: Fibroid 時間: 2025-3-29 11:23
Huidong Xie,Bo Zhou,Xiongchao Chen,Xueqi Guo,Stephanie Thorn,Yi-Hwa Liu,Ge Wang,Albert Sinusas,Chi L作者: 注入 時間: 2025-3-29 18:16
CDiffMR: Can We Replace the?Gaussian Noise with?K-Space Undersampling for?Fast MRI?ave gained burgeoning interests as a novel group of deep learning-based generative methods. These methods seek to sample data points that belong to a target distribution from a Gaussian distribution, which has been successfully extended to MRI reconstruction. In this work, we proposed a Cold Diffusi作者: Volatile-Oils 時間: 2025-3-29 19:50
Learning Deep Intensity Field for?Extremely Sparse-View CBCT Reconstructionsed generation methods represent the CT as discrete voxels, resulting in high memory requirements and limited spatial resolution due to the use of 3D decoders. In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstru作者: syncope 時間: 2025-3-30 02:42 作者: Irksome 時間: 2025-3-30 07:36 作者: 言外之意 時間: 2025-3-30 09:12
An Explainable Deep Framework: Towards Task-Specific Fusion for?Multi-to-One MRI Synthesisvarious reasons. To address this issue, MRI synthesis is a potential solution. Recent deep learning-based methods have achieved good performance in combining multiple available sequences for missing sequence synthesis. Despite their success, these methods lack the ability to quantify the contributio作者: Petechiae 時間: 2025-3-30 13:23
Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation they often generate inaccurate mappings that shift the anatomy. This problem is further exacerbated when the images from the source and target modalities are heavily misaligned. Recently, current methods have aimed to address this issue by incorporating a supplementary segmentation network. Unfortu作者: Bph773 時間: 2025-3-30 17:32
Alias-Free Co-modulated Network for?Cross-Modality Synthesis and?Super-Resolution of?MR Imagesed modality images and reduce slice thickness for magnetic resonance imaging (MRI), respectively. It is also desirable to build a network for simultaneous cross-modality and super-resolution (CMSR) so as to further bridge the gap between clinical scenarios and research studies. However, these works 作者: 揭穿真相 時間: 2025-3-30 23:48
Multi-perspective Adaptive Iteration Network for?Metal Artifact Reductionality of metal-corrupted image remains a challenge. Although the deep learning-based MAR methods have achieved impressive success, their interpretability and generalizability need further improvement. It is found that metal artifacts mainly concentrate in high frequency, and their distributions in t作者: 壓迫 時間: 2025-3-31 01:32 作者: Tinea-Capitis 時間: 2025-3-31 07:07
Low-Dose CT Image Super-Resolution Network with?Dual-Guidance Feature Distillation and?Dual-Path Cons have been proposed to deal with those issues, but there still exists drawbacks: (1) convolution without guidance causes essential information not highlighted; (2) features with fixed-resolution lose the attention to multi-scale information; (3) single super-resolution module fails to balance detai作者: CHARM 時間: 2025-3-31 10:34 作者: 歪曲道理 時間: 2025-3-31 14:19 作者: 狗舍 時間: 2025-3-31 20:44 作者: Self-Help-Group 時間: 2025-3-31 23:07
Feature-Conditioned Cascaded Video Diffusion Models for?Precise Echocardiogram Synthesisobustness, domain transfer, causal modelling, and operator training become approachable through synthetic data. Especially, heavily operator-dependant modalities like Ultrasound imaging require robust frameworks for image and video generation. So far, video generation has only been possible by provi作者: 鋼盔 時間: 2025-4-1 03:49
DULDA: Dual-Domain Unsupervised Learned Descent Algorithm for?PET Image Reconstructionning paradigm, which rely heavily on the availability of high-quality training labels. In particular, the long scanning time required and high radiation exposure associated with PET scans make obtaining these labels impractical. In this paper, we propose a dual-domain unsupervised PET image reconstr作者: FLINT 時間: 2025-4-1 06:08
Transformer-Based Dual-Domain Network for?Few-View Dedicated Cardiac SPECT Image Reconstructionsis of CVDs. The GE 530/570c dedicated cardiac SPECT scanners adopt a stationary geometry to simultaneously acquire 19 projections to increase sensitivity and achieve dynamic imaging. However, the limited amount of angular sampling negatively affects image quality. Deep learning methods can be implem作者: PAN 時間: 2025-4-1 10:35
Learned Alternating Minimization Algorithm for?Dual-Domain Sparse-View CT Reconstruction a variational model for CT reconstruction with learnable nonsmooth nonconvex regularizers, which are parameterized as composite functions of deep networks in both image and sinogram domains. To minimize the objective of the model, we incorporate the smoothing technique and residual learning archite作者: 極小量 時間: 2025-4-1 16:57
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinogramscting standard-dose PET (SPET) images from low-dose PET (LPET) sinograms directly. However, current methods often neglect boundaries during sinogram-to-image reconstruction, resulting in high-frequency distortion in the frequency domain and diminished or fuzzy edges in the reconstructed images. Furt
