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: Body-Mass-Index 時(shí)間: 2025-3-21 18:05
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: 明智的人 時(shí)間: 2025-3-22 00:03
Marleen de Bruijne,Philippe C. Cattin,Caroline Ess作者: 表被動(dòng) 時(shí)間: 2025-3-22 02:04
0302-9743 and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality..Part V: computer aided diagnosis; i978-3-030-87195-6978-3-030-87196-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 飛鏢 時(shí)間: 2025-3-22 05:30
Junshen Xu,Elfar Adalsteinssone.Durch diese umfassende Herangehensweise erschlie?en sich dem Leser verschiedene M?glichkeiten des Online-Marketings, wie zum Beispiel:.E-Mail-Marketing.Social-Media-Marketing.Suchmaschinenmarketing und -optim978-3-658-25360-8Series ISSN 2363-9539 Series E-ISSN 2363-9547 作者: 搏斗 時(shí)間: 2025-3-22 09:31
Jiahong Ouyang,Qingyu Zhao,Ehsan Adeli,Edith V. Sullivan,Adolf Pfefferbaum,Greg Zaharchuk,Kilian M. 作者: Mercantile 時(shí)間: 2025-3-22 13:37
Rhydian Windsor,Amir Jamaludin,Timor Kadir,Andrew Zisserman作者: 多嘴多舌 時(shí)間: 2025-3-22 20:35 作者: 奇怪 時(shí)間: 2025-3-23 00:23 作者: 閃光東本 時(shí)間: 2025-3-23 02:57 作者: Notify 時(shí)間: 2025-3-23 08:48 作者: 惡心 時(shí)間: 2025-3-23 11:09 作者: Prostaglandins 時(shí)間: 2025-3-23 17:47 作者: dialect 時(shí)間: 2025-3-23 20:07
Benoit Dufumier,Pietro Gori,Julie Victor,Antoine Grigis,Michele Wessa,Paolo Brambilla,Pauline Favre,kenntnishaltung im Anschluss an die Dokumentarische Methode nach Bohnsack und Nohl eingenommen (vgl. Kapitel 4) sowie sechs Gruppendiskussionen in fünf Wohnorganisationen erhoben, dokumentarisch ausgewertet und hiervon ausgehend mehrere Typiken entwickelt (vgl. Kapitel 5-9).作者: enchant 時(shí)間: 2025-3-23 23:45 作者: 沙發(fā) 時(shí)間: 2025-3-24 05:14
Xiaoman Zhang,Shixiang Feng,Yuhang Zhou,Ya Zhang,Yanfeng Wangschnitt 6.4), sprich zum Stadtteil und dort angesiedelten Organisationen, wie zum Beispiel der Offenen Kinder- und Jugendarbeit, war die Idee geboren, auch diese geforder-ten ?ffnungsprozesse innerhalb der schulischen Praxis zu untersuchen. Um diesen Fragen detailliert nachgehen und dichte Beschreib作者: 推延 時(shí)間: 2025-3-24 09:58 作者: TOXIN 時(shí)間: 2025-3-24 14:14 作者: fiscal 時(shí)間: 2025-3-24 17:51
Wenhui Lei,Wei Xu,Ran Gu,Hao Fu,Shaoting Zhang,Shichuan Zhang,Guotai Wangschnitt 6.4), sprich zum Stadtteil und dort angesiedelten Organisationen, wie zum Beispiel der Offenen Kinder- und Jugendarbeit, war die Idee geboren, auch diese geforder-ten ?ffnungsprozesse innerhalb der schulischen Praxis zu untersuchen. Um diesen Fragen detailliert nachgehen und dichte Beschreib作者: 完成才會(huì)征服 時(shí)間: 2025-3-24 22:17
SSLP: Spatial Guided Self-supervised Learning on Pathological Imagesnd . AUC on CAMELYON linear classification and . accuracy fine-tuning on cross-disease classification on NCTCRC, which outperforms previous state-of-the-art algorithm and matches the performance of a supervised counterpart.作者: drusen 時(shí)間: 2025-3-25 00:17
Deformed2Self: Self-supervised Denoising for Dynamic Medical Imagingts without needing the pairwise ground truth of clean images. In the field of multi-image denoising, however, very few works have been done on extracting correlated information from multiple slices for denoising using self-supervised deep learning methods. In this work, we propose Deformed2Self, an 作者: GET 時(shí)間: 2025-3-25 06:19
Imbalance-Aware Self-supervised Learning for 3D Radiomic Representations) balancing the composition of training batches. When combining the learned self-supervised feature with traditional radiomics, we show significant improvement in brain tumor classification and lung cancer staging tasks covering MRI and CT imaging modalities. Codes are available in ..作者: browbeat 時(shí)間: 2025-3-25 10:44 作者: 假 時(shí)間: 2025-3-25 15:00
Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classificationh similar . meta-data with the anchor, assuming they share similar discriminative semantic features.?With our method, a 3D CNN model pre-trained on . multi-site healthy brain MRI scans can extract relevant features for three classification tasks: schizophrenia, bipolar diagnosis and Alzheimer’s dete作者: 直覺沒有 時(shí)間: 2025-3-25 18:41
Self-supervised Longitudinal Neighbourhood Embedding We apply LNE to longitudinal T1w MRIs of two neuroimaging studies: a dataset composed of 274 healthy subjects, and Alzheimer’s Disease Neuroimaging Initiative (ADNI, .). The visualization of the smooth trajectory vector field and superior performance on downstream tasks demonstrate the strength of 作者: Semblance 時(shí)間: 2025-3-25 23:00
SimTriplet: Simple Triplet Representation Learning with a Single GPUg negative samples; and (3) The recent mix precision training is employed to advance the training by only using a single GPU with 16?GB memory. By learning from 79,000 unlabeled pathological patch images, SimTriplet achieved 10.58% better performance compared with supervised learning. It also achiev作者: 仔細(xì)檢查 時(shí)間: 2025-3-26 03:30
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentationations through multi-scale inputs. Moreover, an adversarial learning module is further introduced to learn modality invariant representations from multiple unlabeled source datasets. We demonstrate the effectiveness of our methods on two downstream tasks: i) Brain tumor segmentation, ii) Pancreas tu作者: 憤世嫉俗者 時(shí)間: 2025-3-26 07:11 作者: 銀版照相 時(shí)間: 2025-3-26 12:16 作者: 方便 時(shí)間: 2025-3-26 12:43 作者: 安裝 時(shí)間: 2025-3-26 17:24
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: Exposition 時(shí)間: 2025-3-26 22:29 作者: 我們的面粉 時(shí)間: 2025-3-27 04:41
0302-9743 ational Conference 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 org作者: 生氣地 時(shí)間: 2025-3-27 06:21 作者: reserve 時(shí)間: 2025-3-27 13:00
Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-supervised Learningchmarks, including the ones that are unseen during training. Our results show . of the proposed approach across data from different machines and with different SOIs: a major use case of semi-automatic segmentation methods where fully supervised approaches would normally struggle.作者: 使高興 時(shí)間: 2025-3-27 15:04
Self-supervised Multi-modal Alignment for Whole Body Medical Imaging unsupervised manner. (iii) Finally, we use these registrations to transfer segmentation maps from the DXA scans to the MR scans where they are used to train a network to segment anatomical regions without requiring ground-truth MR examples. To aid further research, our code is publicly available (.).作者: CRUE 時(shí)間: 2025-3-27 19:13 作者: 噴出 時(shí)間: 2025-3-28 01:09 作者: CAND 時(shí)間: 2025-3-28 05:15
One-Shot Medical Landmark Detectionk detector with those predictions. The effectiveness of CC2D is evaluated on a widely-used public dataset of cephalometric landmark detection, which achieves a competitive detection accuracy of 86.25.01% within 4.0 mm, comparable to the state-of-the-art semi-supervised methods using a lot more than one training image. Our code is available at ..作者: 跳脫衣舞的人 時(shí)間: 2025-3-28 09:26 作者: Adulterate 時(shí)間: 2025-3-28 13:26
SSLP: Spatial Guided Self-supervised Learning on Pathological Imagesxpert annotations. However, the performance of SSL algorithms on WSIs has long lagged behind their supervised counterparts. To close this gap, in this paper, we fully explore the intrinsic characteristics of WSIs and propose SSLP: Spatial Guided Self-supervised Learning on Pathological Images. We ar作者: BUDGE 時(shí)間: 2025-3-28 17:20 作者: 屈尊 時(shí)間: 2025-3-28 22:35 作者: Yourself 時(shí)間: 2025-3-28 23:42 作者: helper-T-cells 時(shí)間: 2025-3-29 07:07 作者: peritonitis 時(shí)間: 2025-3-29 08:55 作者: Hallowed 時(shí)間: 2025-3-29 15:05
Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-supervised Learningtion). We show that high accuracy can be achieved by simply propagating the 2D slice segmentation with an affinity matrix between consecutive slices, which can be learnt in a self-supervised manner, namely slice reconstruction. Specifically, we compare our proposed framework, termed as ., with super作者: vasospasm 時(shí)間: 2025-3-29 19:13
Self-supervised Longitudinal Neighbourhood Embeddinging this data via machine learning generally requires a large number of ground-truth labels, which are often missing or expensive to obtain. Reducing the need for labels, we propose a self-supervised strategy for representation learning named Longitudinal Neighborhood Embedding (LNE). Motivated by c作者: 條街道往前推 時(shí)間: 2025-3-29 22:21 作者: overreach 時(shí)間: 2025-3-30 02:31 作者: CHIDE 時(shí)間: 2025-3-30 05:30
Lesion-Based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images to learn feature representations from unlabeled images. However, unlike natural images, the application of contrastive learning to medical images is relatively limited. In this work, we propose a self-supervised framework, namely lesion-based contrastive learning for automated diabetic retinopathy 作者: 百靈鳥 時(shí)間: 2025-3-30 12:03 作者: 共同時(shí)代 時(shí)間: 2025-3-30 15:36 作者: 咒語(yǔ) 時(shí)間: 2025-3-30 16:31
SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classificatih is laborious and time-consuming. In this paper, we aim to develop a hybrid-supervised model generation strategy, called SpineGEM, which can economically generate a high-performing deep learning model for the classification of multiple pathologies of lumbar degeneration disease (LDD). A unique self作者: 發(fā)牢騷 時(shí)間: 2025-3-30 23:26 作者: 我沒有強(qiáng)迫 時(shí)間: 2025-3-31 02:04
Topological Learning and Its Application to Multimodal Brain Network Integrationtional MRI in a coherent statistical framework. Existing multimodal frameworks will inevitably destroy the topological difference of the networks. In this paper, we propose a novel topological learning framework that integrates networks of different topology through persistent homology. Such challen作者: prediabetes 時(shí)間: 2025-3-31 06:43 作者: 舊石器時(shí)代 時(shí)間: 2025-3-31 12:22
Implicit Field Learning for Unsupervised Anomaly Detection in Medical Imagesh, an auto-decoder feed-forward neural network learns the distribution of healthy images in the form of a mapping between spatial coordinates and probabilities over a proxy for tissue types. At inference time, the learnt distribution is used to retrieve, from a given test image, a restoration, i.e. 作者: 偽造 時(shí)間: 2025-3-31 14:30 作者: 檔案 時(shí)間: 2025-3-31 19:07
Dani Kiyasseh,Albert Swiston,Ronghua Chen,Antong Chenrundlagen des Online-Marketings. Kurze Lerneinheiten, übersichtliche didaktische Module sowie die begleitende Lernkontrolle sorgen für eine nachhaltige Wissensvermittlung.?.Es richtet sich an alle, die sich im Rahmen ihrer Aus- und Weiterbildung oder ihrer beruflichen Praxis (etwa in einer Webagentu作者: Optometrist 時(shí)間: 2025-3-31 22:55
Junshen Xu,Elfar Adalsteinssonerende und Nebenfachstudenten im Fachbereich Marketing.Reque.Ein Buch zur Einführung in alle Facetten des Online-Marketings.Dieses Buch vermittelt einen anschaulichen und praxisorientierten überblick über die Grundlagen des Online-Marketings. Kurze Lerneinheiten, übersichtliche didaktische Module so
