Overview of the HECKTOR Challenge at MICCAI 2021: Automatic
Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
CCUT-Net: Pixel-wise Global Context Channel Attention UT-Net for head and neck tumor segmentation
A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images
Automatic Segmentation of Head and Neck (H&N) Primary Tumors in PET and CT images using 3D-Inception-ResNet Model
The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network
Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images
PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT
The Head and Neck Tumor Segmentation based on 3D U-Net: 3D U-net applied to Simple Attention Module for Head and Neck tumor segmentation in PET and CT images
Skip-SCSE Multi-Scale Attention and Co-Learning method for Oropharyngeal Tumor Segmentation on multi-modal PET-CT images
Head and Neck Cancer Primary Tumor Auto Segmentation using Model Ensembling of Deep Learning in PET/CT Images
Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation
Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model
Deep learning based GTV delineation and progression free survival risk score prediction for head and neck cancer patients
Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer
PET/CT Head and Neck tumor segmentation and Progression Free Survival prediction using Deep and Machine learning techniques
Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT images
Multimodal PET/CT Tumour Segmentation and Progression-Free Survival Prediction using a Full-scale UNet with Attention
Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer
Fusion-Based head and neck Tumor Segmentation and Survival prediction using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems
Head and Neck Primary Tumor Segmentation using Deep Neural Networks and Adaptive Ensembling
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks
Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk Prediction
Deep Supervoxel Segmentation Survival Anaylsis in Head and Neck Cancer Patients
A Hybrid Radiomics Approach to Modeling Progression-free Survival in Head and Neck Cancers
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data
Progression Free Survival Prediction for Head and Neck Cancer using Deep Learning based on Clinical and PET/CT Imaging Data
Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma
Self-supervised multi-modality image feature extraction for the progression free survival prediction in head and neck cancer
Comparing deep learning and conventional machine learning for outcome prediction of head and neck cancer in PET/CT.