標題: Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee [打印本頁] 作者: 武士精神 時間: 2025-3-21 20:06
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022影響因子(影響力)
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022影響因子(影響力)學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022網(wǎng)絡公開度
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022網(wǎng)絡公開度學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022被引頻次
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022被引頻次學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022年度引用
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022年度引用學科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022讀者反饋
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022讀者反饋學科排名
作者: 感激小女 時間: 2025-3-21 21:10 作者: 信任 時間: 2025-3-22 03:00
UNeXt: MLP-Based Rapid Medical Image Segmentation Network be effectively adopted for rapid image segmentation in point-of-care applications as they are parameter-heavy, computationally complex and slow to use. To this end, we propose UNeXt which is a Convolutional multilayer perceptron (MLP) based network for image segmentation. We design UNeXt in an effe作者: MOAN 時間: 2025-3-22 06:19
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationred pixels near the adhesive edges or in the low-contrast regions. To address the issues, we advocate to firstly constrain the consistency of pixels with and without strong perturbations to apply a sufficient smoothness constraint and further encourage the class-level separation to exploit the low-e作者: Scintigraphy 時間: 2025-3-22 11:51
Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention studies choose a single annotation as the learning target by default, but they waste valuable information of consensus or disagreements ingrained in the multiple annotations. This paper proposes an Uncertainty-Guided Segmentation Network (UGS-Net), which learns the rich visual features from the reg作者: CAGE 時間: 2025-3-22 13:15
Thoracic Lymph Node Segmentation in?CT Imaging via?Lymph Node Station Stratification and?Size Encodiology and oncology workflows. The high demanding of clinical expertise and prohibitive laboring cost motivate the automated approaches. Previous works focus on extracting effective LN imaging features and/or exploiting the anatomical priors to help LN segmentation. However, the performance in genera作者: 驚惶 時間: 2025-3-22 19:52 作者: 斷斷續(xù)續(xù) 時間: 2025-3-23 00:39 作者: hallow 時間: 2025-3-23 02:56
Stroke Lesion Segmentation from?Low-Quality and?Few-Shot MRIs via?Similarity-Weighted Self-ensemblinsegmentation methods have the great potential to improve the medical resource imbalance and reduce stroke risk in these countries, existing segmentation studies are difficult to be deployed in these low-resource settings because they have such high requirements for the data amount (plenty-shot) and 作者: exquisite 時間: 2025-3-23 08:54 作者: 蹣跚 時間: 2025-3-23 13:15 作者: irradicable 時間: 2025-3-23 14:41
Multimodal Brain Tumor Segmentation Using Contrastive Learning Based Feature Comparison with?Monomodl structure to enhance the capacity of learning tumor-related features, while other valuable related information, such as normal brain appearance, is often ignored. Inspired by the fact that radiologists are often trained to compare with normal tissues when identifying tumor regions, in this paper, 作者: Liberate 時間: 2025-3-23 20:38
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation techniques have recently achieved promising cross-modality medical image segmentation by transferring knowledge from a label-rich source domain to an unlabeled target domain. However, it is also difficult to collect annotations from the source domain in many clinical applications, rendering most pr作者: MEAN 時間: 2025-3-23 23:20
NestedFormer: Nested Modality-Aware Transformer for?Brain Tumor Segmentationevious multi-modal MRI segmentation methods usually perform modal fusion by concatenating multi-modal MRIs at an early/middle stage of the network, which hardly explores non-linear dependencies between modalities. In this work, we propose a novel Nested Modality-Aware Transformer (NestedFormer) to e作者: 溫室 時間: 2025-3-24 06:26
MaxStyle: Adversarial Style Composition for?Robust Medical Image Segmentationme domain, yet their performance can degrade significantly on unseen domains, which hinders the deployment of CNNs in many clinical scenarios. Most existing works improve model out-of-domain (OOD) robustness by collecting multi-domain datasets for training, which is expensive and may not always be f作者: ciliary-body 時間: 2025-3-24 09:06
A Robust Volumetric Transformer for?Accurate 3D Tumor Segmentationobal spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode local and global cues, while the decoder employs a parallel self and cross attention formulation to capture fine details for boun作者: Indolent 時間: 2025-3-24 12:16
Usable Region Estimate for?Assessing Practical Usability of?Medical Image Segmentation Models’s predictions can be used/trusted. We first propose a measure, Correctness-Confidence Rank Correlation (CCRC), to capture how predictions’ confidence estimates correlate with their correctness scores in rank. A model with a high value of CCRC means its prediction confidences reliably suggest which 作者: Harrowing 時間: 2025-3-24 18:51 作者: 指派 時間: 2025-3-24 19:48
a small dispute cannot be settled, that we cannot simply tick it off and put it aside, we are at the starting point of the conflict spiral. Reliable signs of an incipient conflict show up in psychological, mental changes and later in physical symptoms. We think about this conflict more and more ofte作者: 星球的光亮度 時間: 2025-3-24 23:35
lly uncover new innovative ideas.Practical preparation of th.The textbook contains a comprehensive presentation of tools that can be used to systematically generate innovative ideas for new business growth ("opportunities"). In practice, it can be observed that companies make considerable efforts to作者: Moderate 時間: 2025-3-25 04:48
Raja Ebsim,Benjamin G. Faber,Fiona Saunders,Monika Frysz,Jenny Gregory,Nicholas C. Harvey,Jonathan Hnsp?dagogischen, universit?ren Aus- und Weiterbildungsprogramms auseinander. Im Rahmen eines einj?hrig angelegten Fortbildungsprogramms wurden Studierende verschiedener Masterstudieng?nge wissenschaftlich fundiert sowie problem- und erfahrungsorientiert darauf vorbereitet, regionale Vernetzungsproze作者: 兵團 時間: 2025-3-25 08:49
Wookjin Choi,Navdeep Dahiya,Saad Nadeemnsp?dagogischen, universit?ren Aus- und Weiterbildungsprogramms auseinander. Im Rahmen eines einj?hrig angelegten Fortbildungsprogramms wurden Studierende verschiedener Masterstudieng?nge wissenschaftlich fundiert sowie problem- und erfahrungsorientiert darauf vorbereitet, regionale Vernetzungsproze作者: 你正派 時間: 2025-3-25 15:34
Xiaofeng Liu,Fangxu Xing,Nadya Shusharina,Ruth Lim,C.-C. Jay Kuo,Georges El Fakhri,Jonghye Wootisiert die bundesdeutschen Reproduktionsbedingungen.In den 1970er Jahren konzipierte Ekkehard Krippendorff seinen polit?konomischen Ansatz der kritischen Friedensforschung. Seine These lautet, dass Au?enpolitik und der Einsatz milit?rischer Gewalt in der Au?enpolitik auf den objektiven Interessen d作者: Painstaking 時間: 2025-3-25 19:18 作者: inhibit 時間: 2025-3-25 20:18
Dong Zhang,Raymond Confidence,Udunna Anazodolenz und Geschlechtergleichstellung nachgegangen wird. In diesem Zusammenhang setzt sich die Analyse theoretisch, methodologisch und empirisch mit den diskursiven Sicht- und Sprechbarkeiten zum ?wissenschaftlichen Nachwuchs‘a(chǎn)useinander, die am Kreuzungspunkt von Exzellenz und Geschlechtergleichstell作者: 著名 時間: 2025-3-26 03:06 作者: CAGE 時間: 2025-3-26 05:01
Yao Zhang,Nanjun He,Jiawei Yang,Yuexiang Li,Dong Wei,Yawen Huang,Yang Zhang,Zhiqiang He,Yefeng Zhengion design process. First, an introducing part with an overview on the current challenging aspects on standardization in terms of building with paper are discussed. The focus is mainly set on the mechanical properties. Building physical design aspects like e.g. fire retardation, humidity and biologi作者: GLOSS 時間: 2025-3-26 08:58 作者: macrophage 時間: 2025-3-26 16:28
Chen Chen,Zeju Li,Cheng Ouyang,Matthew Sinclair,Wenjia Bai,Daniel Rueckertr Polizei, Plattenfirmen, etc.Enth?lt schwer zug?ngliche TexDie Beitr?ge dieses Bandes sind in der einen oder anderen Weise von zwei Begriffen aus dem Arsenal der soziologischen Systemtheorie Niklas Luhmanns und speziell seiner Organisationssoziologie inspiriert: dem Begriff der Grenzstelle und dem 作者: arbiter 時間: 2025-3-26 16:51 作者: 控制 時間: 2025-3-27 00:24
Yizhe Zhang,Suraj Mishra,Peixian Liang,Hao Zheng,Danny Z. Chenrtriebskanal oder als Gesch?ftsmodell. So haben Online-Marketing-Aktivit?ten nochmals stark an Bedeutung gewonnen und bilden mittlerweile mit ca.?58?% weltweit den gr??ten Anteil der Gesamtwerbeausgaben – eine Steigerung von ca.?40?% seit der Erstauflage dieses Buchs in 2019. Eine Fülle von untersch作者: 善變 時間: 2025-3-27 04:06
Zechen Zhao,Heran Yang,Jian Sunund empirische Forschung belegbar gemacht.Online-MarketingakDieses Buch bietet für den Einsatz und die Ausgestaltung vielf?ltiger Online-Marketing-Instrumente und deren Erfolgsmessung konkrete Hilfestellung. Die Autoren haben dazu eine bislang einzigartige übersicht über die Kennzahlen für die wicht作者: 釋放 時間: 2025-3-27 09:19 作者: 史前 時間: 2025-3-27 09:29
Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention Attention Module to enhance the learning of the nodule boundary and density differences. Experimental results show that our method can predict the nodule regions with different uncertainty levels and achieve superior performance in the LIDC-IDRI dataset.作者: BILK 時間: 2025-3-27 13:53 作者: prostatitis 時間: 2025-3-27 21:18
0302-9743 ational Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022..The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in作者: Flawless 時間: 2025-3-28 01:18 作者: alleviate 時間: 2025-3-28 02:29
https://doi.org/10.1007/978-3-031-16443-9artificial intelligence; bioinformatics; computer vision; decision support systems; image analysis; image作者: 和諧 時間: 2025-3-28 07:29 作者: 知識分子 時間: 2025-3-28 12:33
978-3-031-16442-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: ARBOR 時間: 2025-3-28 16:16 作者: 芭蕾舞女演員 時間: 2025-3-28 22:46
0302-9743 reconstruction;.Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; mach978-3-031-16442-2978-3-031-16443-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 容易生皺紋 時間: 2025-3-29 01:34
incorporates the latest research findings, some examples have been updated and supplemented, and the book has been expanded to include practical application tasks for teaching and practical workshops..978-3-658-39810-1978-3-658-39811-8作者: 善于騙人 時間: 2025-3-29 05:52 作者: prosperity 時間: 2025-3-29 09:07 作者: 手工藝品 時間: 2025-3-29 14:04
Dazhou Guo,Jia Ge,Ke Yan,Puyang Wang,Zhuotun Zhu,Dandan Zheng,Xian-Sheng Hua,Le Lu,Tsung-Ying Ho,Xia作者: 收養(yǎng) 時間: 2025-3-29 17:00
Ziyuan Zhao,Fangcheng Zhou,Zeng Zeng,Cuntai Guan,S. Kevin Zhou作者: Explicate 時間: 2025-3-29 22:53 作者: 外星人 時間: 2025-3-30 02:10
Automatic Segmentation of?Hip Osteophytes in?DXA Scans Using U-Netscetabulum. The system achieved sensitivity, specificity, and average Dice scores (±std) of (0.98, 0.92, .) for the superior femoral head [793 DXAs], (0.96, 0.85, .) for the inferior femoral head [409 DXAs], and (0.94, 0.73, .) for the lateral acetabulum [760 DXAs]. This work enables large-scale gene作者: ADOPT 時間: 2025-3-30 06:27 作者: Charitable 時間: 2025-3-30 08:39
UNeXt: MLP-Based Rapid Medical Image Segmentation Networkd computational complexity while being able to result in a better representation to help segmentation. The network also consists of skip connections between various levels of encoder and decoder. We test UNeXt on multiple medical image segmentation datasets and show that we reduce the number of para作者: 毗鄰 時間: 2025-3-30 13:37
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationding high-quality prototypes, in order to make each class distribution compact and separate different classes. We evaluated our SS-Net against five recent methods on the public LA and ACDC datasets. Extensive experimental results under two semi-supervised settings demonstrate the superiority of our 作者: carotenoids 時間: 2025-3-30 17:05 作者: Pcos971 時間: 2025-3-30 23:14 作者: 熱情贊揚 時間: 2025-3-31 01:46
Stroke Lesion Segmentation from?Low-Quality and?Few-Shot MRIs via?Similarity-Weighted Self-ensemblinefine the coarse prediction via focusing on the ambiguous regions. To overcome the few-shot challenge, a new Soft Distribution-aware Updating strategy trains the Identify-to-Discern Network in the direction beneficial to tumor segmentation via respective optimizing schemes and adaptive similarity ev作者: Guaff豪情痛飲 時間: 2025-3-31 07:40
Edge-Oriented Point-Cloud Transformer for?3D Intracranial Aneurysm Segmentationion graph is constructed where connections across the edge are prohibited, thereby dissimilating contexts of points belonging to different categories. Upon that, graph convolution is performed to refine the confusing features via information exchange with dissimilated contexts. In IHE, to further re作者: Additive 時間: 2025-3-31 10:09
mmFormer: Multimodal Medical Transformer for?Incomplete Multimodal Learning of?Brain Tumor Segmentat semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation. Besides, auxiliary regularizers are introduced in both encoder and decoder to further enhance the model’s robustness to incompl作者: 緩解 時間: 2025-3-31 15:33
Multimodal Brain Tumor Segmentation Using Contrastive Learning Based Feature Comparison with?Monomodto solve incomparable issue between features learned from multimodal and monomodal images. In the experiments, both in-house and public (BraTS2019) multimodal tumor brain image datasets are used to evaluate our proposed framework, demonstrating better performance compared to the state-of-the-art met作者: 做方舟 時間: 2025-3-31 21:28 作者: 口訣法 時間: 2025-3-31 22:21
NestedFormer: Nested Modality-Aware Transformer for?Brain Tumor Segmentationsted Modality-aware Feature Aggregation (NMaFA) module, which enhances long-term dependencies within individual modalities via a tri-orientated spatial-attention transformer, and further complements key contextual information among modalities via a cross-modality attention transformer. Extensive exp作者: Frequency 時間: 2025-4-1 04:40 作者: 入伍儀式 時間: 2025-4-1 06:17
Usable Region Estimate for?Assessing Practical Usability of?Medical Image Segmentation Modelstion on to what extent a model’s predictions are usable. In addition, the sizes of usable regions (UR) can be utilized to compare models: A model with a larger UR can be taken as a more usable and hence better model. Experiments on six datasets validate that the proposed evaluation methods perform w作者: 漂浮 時間: 2025-4-1 10:21
Modality-Adaptive Feature Interaction for?Brain Tumor Segmentation with?Missing Modalitiesg situations. Applying MFI with multi-modal code in different stages of a U-shaped architecture, we design a novel network U-Net-MFI to interact multi-modal features hierarchically and adaptively for brain tumor segmentation with missing modality(ies). Experiments show that our model outperforms the作者: Default 時間: 2025-4-1 18:05
