Titlebook: Head and Neck Tumor Segmentation and Outcome Prediction; Third Challenge, HEC Vincent Andrearczyk,Valentin Oreiller,Adrien Depeu Conference

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,Stacking Feature Maps of Multi-scaled Medical Images in U-Net for 3D Head and Neck Tumor Segmentatihe medical domain, it remains as challenging tasks since medical data is heterogeneous, multi-level, and multi-scale. Head and Neck Tumor Segmentation Challenge (HECKTOR) provides a platform to apply machine learning techniques to the medical image domain. HECKTOR 2022 provides positron emission tom

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,A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors nodes (GTVn) from PET/CT images provided by the HEad and neCK TumOR segmentation challenge (HECKTOR) 2022. We utilized a multiclass Dice Loss for model training which was minimized using the AMSGrad variant of the Adam algorithm optimizer. We trained our 2D models on the axial slices of the images

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,Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approual task performed by radiation oncologists. Deep Learning (DL) algorithms have shown potential in creating automatic segmentations, reducing delineation time and inter-observer variation. The aim of this work was to create automatic segmentations of primary tumors (GTVp) and pathological lymph node

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,Simplicity Is All You Need: Out-of-the-Box nnUNet Followed by Binary-Weighted Radiomic Model for Semarkers towards personalized medicine. In this paper, we propose a pipeline to segment the primary and metastatic lymph nodes from fluorodeoxyglucose (FDG) positron emission tomography and computed tomography (PET/CT) head and neck (H &N) images and then predict recurrence free survival (RFS) based

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Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer,ds have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on m

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