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

武士精神

书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022影响因子(影响力)




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022影响因子(影响力)学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022网络公开度




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022网络公开度学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022被引频次




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022被引频次学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022年度引用




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022年度引用学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022读者反馈




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2022读者反馈学科排名

感激小女

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

惊惶

断断续续

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