標(biāo)題: Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow [打印本頁] 作者: GALL 時(shí)間: 2025-3-21 17:47
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影響因子(影響力)
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影響因子(影響力)學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020網(wǎng)絡(luò)公開度
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引頻次
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引頻次學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020讀者反饋
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020讀者反饋學(xué)科排名
作者: 江湖騙子 時(shí)間: 2025-3-21 20:35
Rui Huang,Yuanjie Zheng,Zhiqiang Hu,Shaoting Zhang,Hongsheng Lihe Diskurse.Mit Anwendungsbezügen für die Entscheidungs- undDer vorliegende Text diskutiert sinntheoretische Grundlagen der neueren Organisationsforschung und macht diese durch die Analyse erkenntnisleitender Begriffe aktueller organisationswissenschaftlicher Diskurse (Kognition, Institution, Praxis作者: 錢財(cái) 時(shí)間: 2025-3-22 02:40
Deep Volumetric Universal Lesion Detection Using Light-Weight Pseudo 3D Convolution and Surface Poinicians. Computer-aided lesion/significant-findings detection techniques are at the core of medical imaging, which remain very challenging due to the tremendously large variability of lesion appearance, location and size distributions in 3D imaging. In this work, we propose a novel deep anchor-free o作者: Vasoconstrictor 時(shí)間: 2025-3-22 07:59
DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision, brain atrophy or changes in the pleural space due to pleural effusions. In this work, we present a weakly supervised and detail-preserving method that is able to detect structural changes of existing anatomical structures. In contrast to standard anomaly detection methods, our method extracts info作者: Additive 時(shí)間: 2025-3-22 12:32 作者: 嫌惡 時(shí)間: 2025-3-22 13:56
CircleNet: Anchor-Free Glomerulus Detection with Circle RepresentationcleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus. Different from the traditional bounding box based detection method, the bounding circle (1) reduces the degrees of freedom of detection representation, (2) is naturally rotation inva作者: 健壯 時(shí)間: 2025-3-22 18:45 作者: 坦白 時(shí)間: 2025-3-23 00:27 作者: Costume 時(shí)間: 2025-3-23 02:42 作者: EWER 時(shí)間: 2025-3-23 08:56 作者: 哪有黃油 時(shí)間: 2025-3-23 11:18 作者: insular 時(shí)間: 2025-3-23 15:01
DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentationever, due to anisotropic resolution and ambiguous border (e.g., right ventricular endocardium), existing methods suffer from the degradation of accuracy and robustness in 3D cardiac MRI video segmentation. In this paper, we propose a novel . (DeU-Net) to fully exploit spatio-temporal information fro作者: GIST 時(shí)間: 2025-3-23 22:06 作者: maculated 時(shí)間: 2025-3-24 01:00
Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shapncorporates the continuous spatial information of the target by introducing a spatially encoded (SE) loss based on the distance transform map. Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for dee作者: 細(xì)微的差異 時(shí)間: 2025-3-24 05:55 作者: Inflated 時(shí)間: 2025-3-24 06:45
TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in UltrasoundU) and anesthesia. GA in ultrasound images often show substantial differences in both shape and texture among subjects, leading to a challenging task of automated segmentation. To the best of our knowledge, no work has been published for this task. Meanwhile, dice similarity coefficient (DSC) based 作者: bizarre 時(shí)間: 2025-3-24 13:49 作者: 蜿蜒而流 時(shí)間: 2025-3-24 14:52
Suggestive Annotation of Brain Tumour Images with Gradient-Guided Samplingcation tasks. As a data-driven science, the success of machine learning, in particular supervised learning, largely depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire. It takes a substantial amount of time and作者: 逃避責(zé)任 時(shí)間: 2025-3-24 21:57
Pay More Attention to Discontinuity for Medical Image Segmentationrogress has been made recently. Yet, most existing segmentation methods still struggle at discontinuity positions (including region boundary and discontinuity within regions), especially when generalized to unseen datasets. In particular, discontinuity within regions and being close to the real regi作者: 臨時(shí)抱佛腳 時(shí)間: 2025-3-25 03:10
Learning 3D Features with 2D CNNs via Surface Projection for CT Volume SegmentationN) to learn 3D features. Existing methods hence learn 3D features by still relying on 2D CNNs while attempting to consider more 2D slices, but up until now it is difficulty for them to consider the whole volumetric data, resulting in information loss and performance degradation. In this paper, we pr作者: NICHE 時(shí)間: 2025-3-25 05:52 作者: 駕駛 時(shí)間: 2025-3-25 11:24 作者: BAIT 時(shí)間: 2025-3-25 12:51 作者: Indurate 時(shí)間: 2025-3-25 16:20 作者: FID 時(shí)間: 2025-3-25 23:50 作者: 著名 時(shí)間: 2025-3-26 01:23
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/629197.jpg作者: 防御 時(shí)間: 2025-3-26 07:56 作者: ticlopidine 時(shí)間: 2025-3-26 09:23
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020978-3-030-59719-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Calibrate 時(shí)間: 2025-3-26 13:11
Guohao Dong,Yaoxian Zou,Jiaming Jiao,Yuxi Liu,Shuo Liu,Tianzhu Liang,Chaoyue Liu,Zhijie Chen,Lei Zhuukturellen Durchsetzung realisiert und konkretisiert werden. Aus dieser wissenssoziologischen Diskussion ergeben sich sinn-, kommunikations- und medientheoretische Analysen zu Organisations- und Managementstandards, Texten, übersetzung und identit?tsbezogenen Selbstverk?rperungen, in denen der Begri作者: ERUPT 時(shí)間: 2025-3-26 18:09
Rui Huang,Yuanjie Zheng,Zhiqiang Hu,Shaoting Zhang,Hongsheng Liitrag im Forschungsfeld ?(Welt-)Gesellschaft und Organisation“. Insgesamt wird in den Einzelstudien dieses Textes die Relevanz einer erkenntnis-, sozial- und gesellschaftstheoretischen Fundierung der Organisationstheorie auf Basis einer operativen Sinntheorie diskutiert und ausgelotet.978-3-658-17648-8978-3-658-17649-5作者: 變色龍 時(shí)間: 2025-3-26 22:26 作者: addition 時(shí)間: 2025-3-27 01:57 作者: Exposition 時(shí)間: 2025-3-27 09:09
Xiaohong Liu,Kai Wang,Ke Wang,Ting Chen,Kang Zhang,Guangyu Wang作者: 柳樹；枯黃 時(shí)間: 2025-3-27 13:16
Haichun Yang,Ruining Deng,Yuzhe Lu,Zheyu Zhu,Ye Chen,Joseph T. Roland,Le Lu,Bennett A. Landman,Agnes作者: 按等級(jí) 時(shí)間: 2025-3-27 14:20 作者: 議程 時(shí)間: 2025-3-27 19:11
Xiaowei Xu,Tianchen Wang,Jian Zhuang,Haiyun Yuan,Meiping Huang,Jianzheng Cen,Qianjun Jia,Yuhao Dong,作者: 好開玩笑 時(shí)間: 2025-3-28 01:12
Shuo Wang,Giacomo Tarroni,Chen Qin,Yuanhan Mo,Chengliang Dai,Chen Chen,Ben Glocker,Yike Guo,Daniel R作者: VEST 時(shí)間: 2025-3-28 04:08 作者: 調(diào)味品 時(shí)間: 2025-3-28 07:45
Feng Cheng,Cheng Chen,Yukang Wang,Heshui Shi,Yukun Cao,Dandan Tu,Changzheng Zhang,Yongchao Xu作者: senile-dementia 時(shí)間: 2025-3-28 11:14
Sina Amirrajab,Samaneh Abbasi-Sureshjani,Yasmina Al Khalil,Cristian Lorenz,Jürgen Weese,Josien Pluim作者: Fierce 時(shí)間: 2025-3-28 14:43 作者: 全能 時(shí)間: 2025-3-28 19:05
Jiajia Chu,Yajie Chen,Wei Zhou,Heshui Shi,Yukun Cao,Dandan Tu,Richu Jin,Yongchao Xu作者: mechanism 時(shí)間: 2025-3-28 22:57 作者: Postulate 時(shí)間: 2025-3-29 06:32 作者: 合法 時(shí)間: 2025-3-29 08:19 作者: arabesque 時(shí)間: 2025-3-29 12:40 作者: pus840 時(shí)間: 2025-3-29 16:02
Weakly Supervised One-Stage Vision and Language Disease Detection Using Large Scale Pneumonia and Pnrring expression (objects localized in the image using natural language) input for detection that scales in a purely weakly supervised fashion. The architectural modifications address three obstacles – implementing a supervised vision and language detection method in a weakly supervised fashion, inc作者: FECT 時(shí)間: 2025-3-29 19:48 作者: 無法治愈 時(shí)間: 2025-3-30 02:34 作者: vocation 時(shí)間: 2025-3-30 05:12 作者: modish 時(shí)間: 2025-3-30 08:21
Deep Generative Model-Based Quality Control for Cardiac MRI Segmentationed through iterative search in the latent space. The proposed method achieves high prediction accuracy on two publicly available cardiac MRI datasets. Moreover, it shows better generalisation ability than traditional regression-based methods. Our approach provides a real-time and model-agnostic qual作者: moratorium 時(shí)間: 2025-3-30 15:52 作者: Engaging 時(shí)間: 2025-3-30 19:47
Learning Directional Feature Maps for Cardiac MRI Segmentationmentation. The proposed modules are simple yet effective and can be flexibly added to any existing segmentation network without excessively increasing time and space complexity. We evaluate the proposed method on the 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset and a large-scale 作者: 面包屑 時(shí)間: 2025-3-31 00:14
Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shapng small and discrete targets like scars. We evaluated the proposed framework on 60 LGE MRI data from the MICCAI2018 LA challenge. For LA segmentation, the proposed method reduced the mean Hausdorff distance from 36.4?mm to 20.0?mm compared to the 3D basic U-Net using the binary cross-entropy loss. 作者: 功多汁水 時(shí)間: 2025-3-31 04:07
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phanour conditional XCAT-GAN with real images paired with corresponding labels and subsequently at the inference time, we substitute the labels with the XCAT derived ones. Therefore, the trained network accurately transfers the tissue-specific textures to the new label maps. By creating 33 virtual subje作者: 性別 時(shí)間: 2025-3-31 08:10
TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in Ultrasoundxture-wise accuracy in contour area which can reduce overfitting issues caused by using DSC loss alone. Experiments have been performed on 8487 images from 121 patients. Results show that TexNet outperforms state of the art methods with higher accuracy and better consistency. Besides GA, the propose作者: 隼鷹 時(shí)間: 2025-3-31 12:21
Multi-organ Segmentation via Co-training Weight-Averaged Models from Few-Organ Datasets noisy teaching supervisions between the networks, the weighted-averaged models are adopted to produce more reliable soft labels. In addition, a novel region mask is utilized to selectively apply the consistent constraint on the un-annotated organ regions that require collaborative teaching, which f作者: 昏暗 時(shí)間: 2025-3-31 15:09
Pay More Attention to Discontinuity for Medical Image Segmentationver segmentation tasks demonstrate that such a simple approach effectively mitigates the inaccurate segmentation due to discontinuity and achieves noticeable improvements over some state-of-the-art methods.作者: 媽媽不開心 時(shí)間: 2025-3-31 20:30
