Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

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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(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022網(wǎng)絡(luò)公開度




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2022網(wǎng)絡(luò)公開度學(xué)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(xué)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(xué)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(xué)科排名

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信任

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

MOAN

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

Scintigraphy

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

CAGE

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

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斷斷續(xù)續(xù)

hallow

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

exquisite