Titlebook: Applications of Medical Artificial Intelligence; First International Shandong Wu,Behrouz Shabestari,Lei Xing Conference proceedings 2022 T

難免

書目名稱Applications of Medical Artificial Intelligence影響因子(影響力)




書目名稱Applications of Medical Artificial Intelligence影響因子(影響力)學(xué)科排名




書目名稱Applications of Medical Artificial Intelligence網(wǎng)絡(luò)公開度




書目名稱Applications of Medical Artificial Intelligence網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Applications of Medical Artificial Intelligence被引頻次




書目名稱Applications of Medical Artificial Intelligence被引頻次學(xué)科排名




書目名稱Applications of Medical Artificial Intelligence年度引用




書目名稱Applications of Medical Artificial Intelligence年度引用學(xué)科排名




書目名稱Applications of Medical Artificial Intelligence讀者反饋




書目名稱Applications of Medical Artificial Intelligence讀者反饋學(xué)科排名

殺菌劑

現(xiàn)暈光

Leaven

negligence

,ECG-ATK-GAN: Robustness Against Adversarial Attacks on?ECGs Using Conditional Generative Adversariaunction for adversarial perturbation identification and new blocks for discerning and combining out-of-distribution shifts in signals in the learning process for accurately classifying various arrhythmia types. Furthermore, we benchmark our architecture on six different white and black-box attacks a

vitreous-humor

A Deep Learning-Based Interactive Medical Image Segmentation Framework, We propose to introduce a virtual user in the training process, modelled by simulating the user feedback from the current segmentation. We demonstrate our framework on the task of female pelvis MRI segmentation, using a new dataset. We evaluate our framework against existing work with the standard

CURL

,Deep Neural Network Pruning for?Nuclei Instance Segmentation in?Hematoxylin and?Eosin-Stained Histo layer-wise pruning delivers slightly better performance than network-wide pruning for small compression ratios (CRs) while for large CRs, network-wide pruning yields superior performance. For semantic segmentation, deep regression and final instance segmentation, 93.75%, 95%, and 80% of the model w

粉筆

Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Seg DCE-MR images obtained from public dataset. The segmentation results clearly show that the proposed network model provides the most accurate delineation results of the breast cancers in the DCE-MR images. The proposed model can be applied to other clinical practice sensitive to spatial information

華而不實(shí)

collagen