Public articles linked to the same research event.
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery In a single-center retrospective study, an attention-based multi-task deep learning model combining a ResNet+CBAM imaging branch with a DNN+Transformer clinical-feature branch used contrast-enhanced neck CT to jointly predict lymph node malignancy and primary site, achieving in the test set (n=457) an AUC of 0.851 for benign/malignant prediction, a Micro-AUC of 0.819 for primary-site prediction, and Top-1/Top-3 accuracy of 79%/93%, and in a real-world CMSCCUP cohort (172 cases) a Micro-AUC of 0.809 and Top-3 accuracy of 88%, significantly improving junior physicians' predictive accuracy (all P<0.001).
In a single-center retrospective study, an attention-based multi-task deep learning model combining a ResNet+CBAM imaging branch with a DNN+Transformer clinical-feature branch used contrast-enhanced neck CT to jointly predict lymph node malignancy and primary site, achieving in the test set (n=457) an AUC of 0.851 for benign/malignant prediction, a Micro-AUC of 0.819 for primary-site prediction, and Top-1/Top-3 accuracy of 79%/93%, and in a real-world CMSCCUP cohort (172 cases) a Micro-AUC of 0.809 and Top-3 accuracy of 88%, significantly improving junior physicians' predictive accuracy (all P<0.001).
In a single-center retrospective study, an attention-based multi-task deep learning model combining a ResNet+CBAM imaging branch with a DNN+Transformer clinical-feature branch used contrast-enhanced neck CT to jointly predict lymph node malignancy and primary site, achieving in the test set (n=457) an AUC of 0.851 for benign/malignant prediction, a Micro-AUC of 0.819 for primary-site prediction, and Top-1/Top-3 accuracy of 79%/93%, and in a real-world CMSCCUP cohort (172 cases) a Micro-AUC of 0.809 and Top-3 accuracy of 88%, significantly improving junior physicians' predictive accuracy (all P<0.001).
In a single-center retrospective study, an attention-based multi-task deep learning model combining a ResNet+CBAM imaging branch with a DNN+Transformer clinical-feature branch used contrast-enhanced neck CT to jointly predict lymph node malignancy and primary site, achieving in the test set (n=457) an AUC of 0.851 for benign/malignant prediction, a Micro-AUC of 0.819 for primary-site prediction, and Top-1/Top-3 accuracy of 79%/93%, and in a real-world CMSCCUP cohort (172 cases) a Micro-AUC of 0.809 and Top-3 accuracy of 88%, significantly improving junior physicians' predictive accuracy (all P<0.001).