Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer
Mohammad R. Salmanpour(University of British Columbia), Arman Rahmim(BC Cancer Agency), Seyed Masoud Rezaeijo(Heidelberg University), Ghasem Hajianfar(Shaheed Rajaei Cardiovascular Medical and Research Center), Mohammad Mehdi Ghaemi(Kerman University of Medical Sciences)
Cited by 17
Related Papers
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights
|Radiology|2024|210
Head and neck tumor segmentation in PET/CT: The HECKTOR challenge
|Medical Image Analysis|2021|202
Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems
|Diagnostics|2023|88
Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques
|Diagnostics|2023|83
Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer
|Computer Methods and Programs in Biomedicine|2023|78