Keyphrases
Learning Model
100%
Evolutionary Learning
100%
Head-and-neck Squamous Cell Carcinoma (HNSCC)
100%
CT Images
100%
Extranodal Extension
100%
Prediction Model
25%
Radiologists
16%
Clinical Diagnosis
8%
Treatment Strategy
8%
Diagnostic Accuracy
8%
Poor Prognosis
8%
Support Vector Machine
8%
Test Accuracy
8%
Genetic Algorithm
8%
Deep Learning
8%
Gray Level
8%
Parameter Setting
8%
Operative
8%
Bi-objective
8%
Learning Methods
8%
Combinatorial Genetics
8%
Texture Morphology
8%
Feature Parameter
8%
Extension Method
8%
Optimal Feature Selection
8%
Extension Model
8%
3D Morphology
8%
Diagnostic Performance
8%
Radiomics
8%
Lymph Node Dissection
8%
Computer Tomography Image
8%
Daily Clinical Practice
8%
Contrast-enhanced Computed Tomography
8%
Clinical Knowledge
8%
Lack of Transparency
8%
Medicine and Dentistry
Head and Neck Squamous Cell Carcinoma
100%
Evolution
100%
Computer Assisted Tomography
20%
Genetic Algorithm
20%
Diagnostic Accuracy
20%
Radiomics
20%
Diagnostic Performance
20%
Cervical Lymph Node
20%
Retroperitoneal Lymph Node Dissection
20%
Feature Extraction
20%
Neuroscience
Support Vector Machine
100%
Computed Tomography
100%
Lymph Node
100%
Psychology
Learning Model
100%
Clinical Knowledge
100%
Computerized Tomography
100%
Chemical Engineering
Deep Learning Method
100%
Support Vector Machine
100%
Pharmacology, Toxicology and Pharmaceutical Science
Head and Neck Squamous Cell Carcinoma
100%