標(biāo)題: Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; 24th International C Marleen de Bruijne,Philippe C. Cattin,Caroli [打印本頁(yè)] 作者: crusade 時(shí)間: 2025-3-21 17:51
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021影響因子(影響力)
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021影響因子(影響力)學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021網(wǎng)絡(luò)公開(kāi)度
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021被引頻次
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021被引頻次學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021年度引用
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021年度引用學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021讀者反饋
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021讀者反饋學(xué)科排名
作者: corpuscle 時(shí)間: 2025-3-21 21:45 作者: intrude 時(shí)間: 2025-3-22 02:18
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021978-3-030-87240-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 抒情短詩(shī) 時(shí)間: 2025-3-22 08:05 作者: 死亡率 時(shí)間: 2025-3-22 10:04 作者: 疲勞 時(shí)間: 2025-3-22 16:55 作者: commute 時(shí)間: 2025-3-22 20:00 作者: senile-dementia 時(shí)間: 2025-3-23 00:38 作者: 吸氣 時(shí)間: 2025-3-23 02:17 作者: biopsy 時(shí)間: 2025-3-23 09:34
Multiple Meta-model Quantifying for Medical Visual Question Answeringels which provide robust features for medical VQA tasks. Extensively experimental results on two public medical VQA datasets show that our approach achieves superior accuracy in comparison with other state-of-the-art methods, while does not require external data to train meta-models. Source code available at: ..作者: 去掉 時(shí)間: 2025-3-23 12:04 作者: archaeology 時(shí)間: 2025-3-23 17:20 作者: Laconic 時(shí)間: 2025-3-23 19:46 作者: jabber 時(shí)間: 2025-3-24 00:42 作者: Harbor 時(shí)間: 2025-3-24 06:02
mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Reg Transformer captures the relevance between multi-phase CEMRI for multi-phase CEMRI information fusion and selection. Lastly, we proposed a multi-level training strategy, which enables an enhanced loss function to improve the quantification task. The mfTrans-Net is validated on multi-phase CEMRI of 作者: grandiose 時(shí)間: 2025-3-24 09:48 作者: 斜坡 時(shí)間: 2025-3-24 12:12
A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-Domain Cted in turn, and the data is transferred between different modules to realize the knowledge transfer and collaborative training. The final prediction is obtained by a voting result of two classifiers. Experimental results on three pneumonia databases demonstrate the effectiveness of our framework wi作者: arsenal 時(shí)間: 2025-3-24 14:56 作者: muffler 時(shí)間: 2025-3-24 21:13 作者: artifice 時(shí)間: 2025-3-25 01:24 作者: Conclave 時(shí)間: 2025-3-25 05:13
Conditional Training with Bounding Map for Universal Lesion Detectionle; and (ii) adaptively compute size-adaptive BM (ABM) from lesion bounding-box, which is used for improving lesion localization accuracy via ABM-supervised segmentation. Experiments with four state-of-the-art methods show that the proposed approach can bring an almost free detection accuracy improv作者: Crayon 時(shí)間: 2025-3-25 10:35
Focusing on Clinically Interpretable Features: Selective Attention Regularization for Liver Biopsy Ihe distribution of nuclei in each instance. Experiments conducted on the in-house Liver-NAS and public Biopsy4Grading biopsy image datasets show that our method achieves superior classification performance with promising localization results.作者: 貞潔 時(shí)間: 2025-3-25 15:34
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classificationshing apart negative image pairs from different classes. With this regularization, the feature distribution of the student model shows higher intra-class similarity and inter-class variance. Besides, we propose a Categorical Relation Preserving (CRP) loss to distill the teacher’s relational knowledg作者: groggy 時(shí)間: 2025-3-25 19:50 作者: Cognizance 時(shí)間: 2025-3-25 20:51
Conference proceedings 2021achine learning - uncertainty..Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality..Part V: computer aided diagnosis; i作者: 誘惑 時(shí)間: 2025-3-26 01:56 作者: 假 時(shí)間: 2025-3-26 06:52
Marleen de Bruijne,Philippe C. Cattin,Caroline Ess作者: 不透明性 時(shí)間: 2025-3-26 10:54
Jianfeng Zhao,Xiaojiao Xiao,Dengwang Li,Jaron Chong,Zahra Kassam,Bo Chen,Shuo Lion Victor Hugo stammt das Zitat ?Nichts auf der Welt ist so m?chtig wie eine Idee, deren Zeit gekommen ist.“ Die Leser dieses Buches erhielten dazu sehr zahlreiche Anregungen, insbesondere wie sie erfolgreich in ihre neue Führungsrolle ?onboarden“ und auch nachhaltig exzellente Führungspers?nlichkei作者: GROG 時(shí)間: 2025-3-26 13:30
Heqin Zhu,Qingsong Yao,Li Xiao,S. Kevin Zhouc Laloux in seinem Bestsellerbuch ?Reinventing Organizations“ (Laloux 2013), die ein neues Verst?ndnis für Führung fordern und diese Stimmen finden immer mehr Anklang. Sie tauchen bereits sehr oft in Start-up-Unternehmen auf, mit der Konsequenz, dass sie die gro?en und oft noch sehr hierarchisch str作者: 臭名昭著 時(shí)間: 2025-3-26 18:06
Lixin Ren,Shang Wan,Yi Wei,Xiaowei He,Bin Song,Enhua Wuhen den Ratgeber-Charakter..Das Buch richtet sich an Berufsanf?nger im Bereich Mediaplanung und Studierende sowie an Marketing-, Werbe- und Mediaverantwortliche in Unternehmen, die bereits über Kenntnisse des digitalen Marketings verfügen und dazu ein spezielles Fachwissen im Bereich der Online-Medi作者: 并置 時(shí)間: 2025-3-26 21:26
Fei Lyu,Baoyao Yang,Andy J. Ma,Pong C. Yuenxis messen. Mit diesem essential erhalten Sie Anregungen und Methoden an die Hand, mit der Sie in konstruktive Ver?nderungsprozesse für eine offene Unternehmenskultur einsteigen k?nnen..978-3-658-31257-2978-3-658-31258-9Series ISSN 2197-6708 Series E-ISSN 2197-6716 作者: 慎重 時(shí)間: 2025-3-27 02:53 作者: Intersect 時(shí)間: 2025-3-27 05:36
Han Li,Long Chen,Hu Han,Ying Chi,S. Kevin Zhoulgruppen erreichen m?chten..In diesem Kapitel betrachten wir deshalb zuerst Anlass- und Aktionsspenden im digitalen Raum, betrachten dann soziale Medien in Hinblick auf das Online-Fundraising und lernen zum Schluss, was wir vom Crowdfunding für das Online-Fundraising lernen k?nnen.作者: quiet-sleep 時(shí)間: 2025-3-27 10:06
Chong Yin,Siqi Liu,Rui Shao,Pong C. Yuenlgruppen erreichen m?chten..In diesem Kapitel betrachten wir deshalb zuerst Anlass- und Aktionsspenden im digitalen Raum, betrachten dann soziale Medien in Hinblick auf das Online-Fundraising und lernen zum Schluss, was wir vom Crowdfunding für das Online-Fundraising lernen k?nnen.作者: 草本植物 時(shí)間: 2025-3-27 16:31
Xiaohan Xing,Yuenan Hou,Hang Li,Yixuan Yuan,Hongsheng Li,Max Q.-H. Mengum offensichtlich werden darf. Der Beitrag zeigt zweitens, wie Mechanismen der sozialen Trennung diesen Prozess begleiten: Die Kanalisierung einiger zukünftiger Absolvent/innen hin zur Bildung und Vertiefung einer universit?ren Wissenskultur bzw. vieler Absolvent/innen, die einiges an fachlichem Wis作者: carbohydrate 時(shí)間: 2025-3-27 18:54 作者: 掃興 時(shí)間: 2025-3-28 00:34
Yuan-Xing Zhao,Yan-Ming Zhang,Ming Song,Cheng-Lin Liung auf zentrale Grundbegriffe der Organisationssoziologie, wie Zweck-Mittel-Relationierung, Hierarchie, Regeln, Kontrolle, Mitgliedschaft und Grenze angewendet werden kann. Im Zuge dessen werden die disziplin?ren und methodologischen Grenzen zwischen Markt- und Organisationssoziologie einerseits, ab作者: monochromatic 時(shí)間: 2025-3-28 04:40 作者: Accessible 時(shí)間: 2025-3-28 07:27
Xinxin Shan,Ying Wen,Qingli Li,Yue Lu,Haibin Caidierende sowie an Marketing-, Werbe- und Mediaverantwortliche in Unternehmen, die bereits über Kenntnisse des digitalen Marketings verfügen und dazu ein spezielles Fachwissen im Bereich der Online-Medi978-3-658-31211-4978-3-658-31212-1作者: 樹木中 時(shí)間: 2025-3-28 12:54
Dazhou Guo,Xianghua Ye,Jia Ge,Xing Di,Le Lu,Lingyun Huang,Guotong Xie,Jing Xiao,Zhongjie Lu,Ling Pen作者: 使?jié)M足 時(shí)間: 2025-3-28 16:13
Wenting Jiang,Yicheng Jiang,Lu Zhang,Changmiao Wang,Xiaoguang Han,Shuixing Zhang,Xiang Wan,Shuguang 作者: 歪曲道理 時(shí)間: 2025-3-28 21:21 作者: 同義聯(lián)想法 時(shí)間: 2025-3-28 23:53 作者: Throttle 時(shí)間: 2025-3-29 06:37
Churan Wang,Xinwei Sun,Fandong Zhang,Yizhou Yu,Yizhou Wang作者: 倒轉(zhuǎn) 時(shí)間: 2025-3-29 09:25 作者: HERE 時(shí)間: 2025-3-29 11:51
Tuong Do,Binh X. Nguyen,Erman Tjiputra,Minh Tran,Quang D. Tran,Anh Nguyen作者: ANA 時(shí)間: 2025-3-29 18:03 作者: 乞丐 時(shí)間: 2025-3-29 22:48
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos Using Learnable Tes of HCC and accurate evaluation of tumors in clinical practice. Few studies have investigated HCC segmentation from DSA videos. It shows great challenging due to motion artifacts in filming, ambiguous boundaries of tumor regions and high similarity in imaging to other anatomical tissues. In this pa作者: 閃光東本 時(shí)間: 2025-3-30 02:23 作者: assent 時(shí)間: 2025-3-30 06:31
Semi-supervised Learning for Bone Mineral Density Estimation in Hip X-Ray Images limited accessibility of DEXA machines and examinations, osteoporosis is often under-diagnosed and under-treated, leading to increased fragility fracture risks. Thus it is highly desirable to obtain BMDs with alternative cost-effective and more accessible medical imaging examinations such as X-ray 作者: Nutrient 時(shí)間: 2025-3-30 08:41 作者: 時(shí)間等 時(shí)間: 2025-3-30 14:16
Enhanced Breast Lesion Classification via Knowledge Guided Cross-Modal and Semantic Data Augmentatio complementary counterpart. Although an automated breast lesion classification system is desired, training of such a system is constrained by data scarcity and modality imbalance problems due to the lack of SWE devices in rural hospitals. To enhance the diagnosis with only US available, in this work作者: 換話題 時(shí)間: 2025-3-30 19:55
Multiple Meta-model Quantifying for Medical Visual Question Answeringtask. However, most of the existing medical VQA methods rely on external data for transfer learning, while the meta-data within the dataset is not fully utilized. In this paper, we present a new multiple meta-model quantifying method that effectively learns meta-annotation and leverages meaningful f作者: Plaque 時(shí)間: 2025-3-31 00:45
mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Regsses for HCC treatment and prognosis. However, direct automated quantitative measurement using the CNN-based network a still challenging task due to: (1) The lack of ability for capturing long-range dependencies of multi-anatomy in the whole medical image; (2) The lack of mechanism for fusing and se作者: 自愛(ài) 時(shí)間: 2025-3-31 04:33 作者: CRP743 時(shí)間: 2025-3-31 05:41
A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-Domain C labels of target domain, domain adaptation for unsupervised cross-domain classification attracts widespread attention. However, current methods take knowledge transfer model and classification model as two separate training stages, which inadequately considers and utilizes the intrinsic information作者: epidermis 時(shí)間: 2025-3-31 12:55 作者: flutter 時(shí)間: 2025-3-31 17:14
A Segmentation-Assisted Model for Universal Lesion Detection with Partial Labelsnd timely treatment. Recently, deep neural networks have been applied for the ULD task, and existing methods assume that all the training samples are well-annotated. However, the partial label problem is unavoidable when curating large-scale datasets, where only a part of instances are annotated in 作者: Cuisine 時(shí)間: 2025-3-31 21:16
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation inhy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is able to directly leverage large datasets available from health screening programs that contain mostly normal image samples, avoiding the costly manual la作者: Flustered 時(shí)間: 2025-3-31 22:48
Conditional Training with Bounding Map for Universal Lesion Detectionby coarse-to-fine two-stage detection approaches, but such two-stage ULD methods still suffer from issues like imbalance of positive v.s. negative anchors during object proposal and insufficient supervision problem during localization regression and classification of the region of interest (RoI) pro作者: Nuance 時(shí)間: 2025-4-1 02:20
Focusing on Clinically Interpretable Features: Selective Attention Regularization for Liver Biopsy Ir an effective tool for image analysis. However, when applying deep learning methods to smaller histological image datasets, the model may be distracted by dominant normal tissues and ignore critical tissue alterations that pathologists focus on. In this paper, we propose a selective attention regul作者: spinal-stenosis 時(shí)間: 2025-4-1 06:57
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classificationdata. Studies showed that knowledge distillation (KD), especially the mean-teacher framework which is more robust to perturbations, can help mitigate the over-fitting effect. However, directly transferring KD from computer vision to medical image classification yields inferior performance as medical作者: radiograph 時(shí)間: 2025-4-1 12:53
Tensor-Based Multi-index Representation Learning for Major Depression Disorder Detection with RestinfMRI) provides a non-invasive solution for the study of functional brain network abnormalities in MDD patients. Existing studies have shown that multiple indexes derived from rs-fMRI, such as fractional amplitude of low-frequency fluctuations (fALFF), voxel-mirrored homotopic connectivity (VMHC), an作者: legitimate 時(shí)間: 2025-4-1 18:18
Region Ensemble Network for MCI Conversion Prediction with a Relation Regularized Lossormal areas are subtle compared to the size of the whole brain, 2) the features’ dimension is much larger than the number of samples. To tackle these problems, we propose a region ensemble model using a divide and conquer strategy to capture the disease’s finer representation. Specifically, the feat
