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] 作者: Jefferson 時間: 2025-3-21 17:58
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)公開度
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2021網(wǎng)絡(luò)公開度學(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: insert 時間: 2025-3-21 20:36 作者: 心痛 時間: 2025-3-22 03:13
Colorectal Polyp Classification from White-Light Colonoscopy Images via?Domain Alignmentfrom colonoscopy images. Most previous studies attempt to develop models for polyp differentiation using Narrow-Band Imaging (NBI) or other enhanced images. However, the wide range of these models’ applications for clinical work has been limited by the lagging of imaging techniques. Thus, we propose作者: Myocyte 時間: 2025-3-22 07:21 作者: Mindfulness 時間: 2025-3-22 10:14
Deep-Cleansing: Deep-Learning Based Electronic Cleansing in Dual-Energy CT Colonographyc surface in CT colonography (CTC). This paper introduced a deep-learning based EC method in dual-energy CTC (DE-CTC), named “Deep-Cleansing”. First, we calculated the “effective” atomic number (EAN) by fractions of atomic mass number using the low- and high-energy images in DE-CT. Second, multiple 作者: BET 時間: 2025-3-22 15:23 作者: dragon 時間: 2025-3-22 17:21
Synthesis of Contrast-Enhanced Spectral Mammograms from Low-Energy Mammograms Using cGAN-Based Synthhigh predictive value. However, the iodinated contrast media injected during CESM examination can cause adverse reactions, such as allergic reactions, and even cause contra-induced nephropathy. Therefore, iodinated contrast media cannot be used for some patients. To address this problem, we develope作者: 廣大 時間: 2025-3-23 00:24
Self-adversarial Learning for Detection of?Clustered Microcalcifications in?Mammogramso-step paradigm: segmenting each MC and analyzing their spatial distributions to form MC clusters. However, segmentation of MCs cannot avoid low sensitivity or high false positive rate due to their variability in size (sometimes <0.1?mm), brightness, and shape (with diverse surroundings). In this pa作者: lethal 時間: 2025-3-23 03:53
Graph Transformers for Characterization and Interpretation of Surgical Marginse. Considering the interpretability of Transformer models, and the power of graph networks in analyzing the inherent hierarchy of biological signals, a combined approach would be the next generation solution in computer aided interventions. In this study, we propose a framework for classification an作者: DRILL 時間: 2025-3-23 09:14 作者: 大罵 時間: 2025-3-23 13:45 作者: 啞巴 時間: 2025-3-23 15:28
BI-RADS Classification of Calcification on Mammogramser, we present the first deep learning-based six-class BI-RADS classification for each individual calcification in mammograms. We propose an attention ROI generation strategy to highlight calcification features. Moreover, by incorporating malignancy information, the designed new loss function effect作者: FLOUR 時間: 2025-3-23 19:58
Supervised Contrastive Pre-training for Mammographic Triage Screening Modelse Pre-training (SCP) followed by Supervised Fine-tuning (SF) to improve mammographic triage screening models. Our experiments on a large-scale dataset show that the SCP step can effectively learn a better embedding and subsequently improve the final model performance in comparison with the direct su作者: 蒙太奇 時間: 2025-3-24 00:21 作者: explicit 時間: 2025-3-24 03:15 作者: 供過于求 時間: 2025-3-24 08:45 作者: 支架 時間: 2025-3-24 14:23 作者: 過度 時間: 2025-3-24 17:07
s for practical application.Consideration of simplified appr.Arequirement for the safe design of thermoplastic parts is the ability toprecisely predict mechanical behaviour by ?nite element simulations. Typicalexamples include the engineering of relevant components in automotiveapplications. For thi作者: 發(fā)炎 時間: 2025-3-24 19:48
Anuja Vats,Marius Pedersen,Ahmed Mohammed,?istein Hovdements. These depend strongly non-linearly on the strain energy density present in the glass at the time of fracture, which can be converted into fracture energy. Thus, the design and optimization of structural glazing in engineering requires both knowledge of the relevant parameters and mechanisms d作者: RENIN 時間: 2025-3-25 03:03 作者: 陶器 時間: 2025-3-25 05:42 作者: dura-mater 時間: 2025-3-25 10:16 作者: 輕浮思想 時間: 2025-3-25 11:55 作者: 鄙視讀作 時間: 2025-3-25 18:27
Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learningion, we demonstrate the feasibility of our approach and also discuss the future applications of this interactive reconstruction approach. We also test the proposed methodology on public Walnut and Lodopab CT reconstruction datasets to show it can be generalized to CT reconstruction as well.作者: Infelicity 時間: 2025-3-26 00:03 作者: 使?jié)M足 時間: 2025-3-26 02:02 作者: 漂亮才會豪華 時間: 2025-3-26 06:40 作者: 消極詞匯 時間: 2025-3-26 09:57
Conference proceedings 2021nference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*.The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in 作者: Flirtatious 時間: 2025-3-26 14:00
https://doi.org/10.1007/978-3-030-87234-2artificial intelligence; bioinformatics; computer aided diagnosis; computer assisted interventions; comp作者: 記憶法 時間: 2025-3-26 20:10
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/629206.jpg作者: FLING 時間: 2025-3-26 23:51 作者: RADE 時間: 2025-3-27 02:02
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021978-3-030-87234-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: jabber 時間: 2025-3-27 06:27 作者: 的闡明 時間: 2025-3-27 13:06 作者: Insul島 時間: 2025-3-27 16:10 作者: 做方舟 時間: 2025-3-27 20:53
Zihao Liu,Ruiqin Xiong,Tingting Jiangite patent in ., where the fate of the ‘young poets’ and of their inquiry on the foundations of the surrealist movement serves as a vehicle for the interpretation of violence. In . and ., though, the project of examining the poetic, performative condition of the political violence of the twentieth c作者: cornucopia 時間: 2025-3-28 00:53 作者: 泥沼 時間: 2025-3-28 06:05 作者: Aggrandize 時間: 2025-3-28 08:28 作者: Prologue 時間: 2025-3-28 12:37 作者: flex336 時間: 2025-3-28 17:55
Guibo Luo,Tianyu Liu,Bin Li,Michael Zalis,Wenli Cai作者: consolidate 時間: 2025-3-28 19:11
Yanyun Jiang,Yuanjie Zheng,Weikuan Jia,Sutao Song,Yanhui Ding作者: 新娘 時間: 2025-3-28 23:06 作者: LAVA 時間: 2025-3-29 05:10
Amoon Jamzad,Alice Santilli,Faranak Akbarifar,Martin Kaufmann,Kathryn Logan,Julie Wallis,Kevin Ren,S作者: 完成才會征服 時間: 2025-3-29 10:31
Zheren Li,Zhiming Cui,Sheng Wang,Yuji Qi,Xi Ouyang,Qitian Chen,Yuezhi Yang,Zhong Xue,Dinggang Shen,J作者: 小溪 時間: 2025-3-29 11:38 作者: Dappled 時間: 2025-3-29 18:29
Yanbo Zhang,Yuxing Tang,Zhenjie Cao,Mei Han,Jing Xiao,Jie Ma,Peng Chang作者: 有其法作用 時間: 2025-3-29 21:46 作者: DEVIL 時間: 2025-3-30 02:33 作者: 分散 時間: 2025-3-30 05:43 作者: BAN 時間: 2025-3-30 08:58
Non-invasive Assessment of Hepatic Venous Pressure Gradient (HVPG) Based on MR Flow Imaging and Comp simulations [., .]. We apply the latest techniques of phase-contrast magnetic resonance imaging (PC-MRI) and DIXON to obtain the velocity and vessel anatomies at the same time. Besides, we improve the CFD pipeline in regards to the construction of vessel connections and reduction of calculation tim作者: LINES 時間: 2025-3-30 12:55 作者: 和平主義 時間: 2025-3-30 18:12 作者: 否決 時間: 2025-3-31 00:08
Graph Transformers for Characterization and Interpretation of Surgical Marginsalization approaches to facilitate the interpretability. RESULTS: In a 4-fold cross validation experiment, an average classification AUC of 95.6% was achieved, outperforming baseline models. We could also distinguish and visualize clear pattern of attention difference between burns. For instance, ca作者: ALB 時間: 2025-3-31 03:21
Domain Generalization for?Mammography Detection via?Multi-style and Multi-view Contrastive Learning the backbone network is then recalibrated to the downstream task of lesion detection with the specific supervised learning. The proposed method is evaluated with mammograms from four vendors and one unseen public dataset. The experimental results suggest that our approach can effectively improve de作者: 處理 時間: 2025-3-31 06:30
