Titlebook: Clinical Image-Based Procedures; 11th Workshop, CLIP Yufei Chen,Marius George Linguraru,Cristina Oyarzu Conference proceedings 2023 The Ed

表范围

书目名称Clinical Image-Based Procedures影响因子(影响力)




书目名称Clinical Image-Based Procedures影响因子(影响力)学科排名




书目名称Clinical Image-Based Procedures网络公开度




书目名称Clinical Image-Based Procedures网络公开度学科排名




书目名称Clinical Image-Based Procedures被引频次




书目名称Clinical Image-Based Procedures被引频次学科排名




书目名称Clinical Image-Based Procedures年度引用




书目名称Clinical Image-Based Procedures年度引用学科排名




书目名称Clinical Image-Based Procedures读者反馈




书目名称Clinical Image-Based Procedures读者反馈学科排名

collagen

https://doi.org/10.1007/978-981-16-4023-0similar to the target domain for domain adaptation. Compared to the ‘source-target pair’ domain adaptation method using all source domains, this method improves accuracy by up to 10. and reduces computation time by up to 43., based on the SEED-III and SEED-IV datasets.

cloture

,Conditional Domain Adaptation Based on Initial Distribution Discrepancy for EEG Emotion Recognitionsimilar to the target domain for domain adaptation. Compared to the ‘source-target pair’ domain adaptation method using all source domains, this method improves accuracy by up to 10. and reduces computation time by up to 43., based on the SEED-III and SEED-IV datasets.

Hallowed

dominant

Rural Latin America in Transitionhich can extract discriminative features from radio-frequency (RF) signals generated from QUS. Compared with the conventional QUS method using SOS, experimental results indicate that our proposed method achieves superior performance, which can be beneficial to the osteoporosis screening.

ARCH

ARCH

https://doi.org/10.1007/978-981-16-4023-0meters that aid in monitoring ocular and cardiovascular diseases. The results on the given data are comparable to the performance of a trained expert and the methods are already being used in clinical practice.

充气女

不规则的跳动

,Convolutional Redistribution Network for Multi-view Medical Image Diagnosis,acts essential information from multi-view data to generate a series of “good and diverse” pseudo views for integration. The experiment results show that proposed model achieves good performance on pancreatic tumor classification task as well as the OrganMNIST3D classification task of the MedMNIST public datasets.

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