서지주요정보
Head and Neck Tumor Segmentation and Outcome Prediction Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / [electronic resource]
서명 / 저자 Head and Neck Tumor Segmentation and Outcome Prediction [electronic resource] : Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / edited by Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge.
판사항 1st ed. 2023.
발행사항 Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
총서명 Lecture Notes in Computer Science, 1611-3349 ; 13626
Online Access https://doi.org/10.1007/978-3-031-27420-6URL

서지기타정보

서지기타정보
청구기호 TA1501-1820
형태사항 XI, 257 p. 75 illus., 67 illus. in color. online resource.
언어 English
내용 Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT 1 -- Automated head and neck tumor segmentation from 3D PET/CT HECKTOR 2022 challenge report -- A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET Images -- A General Web-based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images -- Octree Boundary Transfiner: Effcient Transformers for Tumor Segmentation Refinement -- Head and Neck Primary Tumor and Lymph Node Auto-Segmentation for PET/CT Scans -- Fusion-based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques -- Stacking Feature Maps of Multi-Scaled Medical Images in U-Net for 3D Head and Neck Tumor Segmentation -- A fine-tuned 3D U-net for primary tumor and affected lymph nodes segmentation in fused multimodal images of oropharyngeal cancer -- A U-Net convolutional neural network with multiclass Dice loss for automated segmentation of tumors and lymph nodes from head and neck cancer PET/CT images -- Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation -- Swin UNETR for tumor and lymph node delineation of multicentre oropharyngeal cancer patients with PET/CT imaging -- Simplicity is All You Need: Out-of-the-Box nnUNet followed by Binary-Weighted Radiomic Model for Segmentation and Outcome Prediction in Head and Neck PET/CT -- Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer -- Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers -- Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images -- LC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning -- Towards Tumour Graph Learning for Survival Prediction in Head Neck Cancer Patients -- Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT Images -- Head and neck cancer localization with Retina Unet for automated segmentation and time-to-event prognosis from PET/CT images -- HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG-PET/CT images -- Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network -- Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer -- Deep learning and radiomics based PET/CT image feature extraction from auto segmented tumor volumes for recurrence-free survival prediction in oropharyngeal cancer patients.
주제 Image processing—Digital techniques.
Computer vision.
Image processing.
Machine learning.
Bioinformatics.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Image Processing.
Machine Learning.
Computational and Systems Biology.
보유판 및 특별호 저록 Springer Nature eBook
Printed edition: 9783031274190 Printed edition: 9783031274213
ISBN 9783031274206
기타 표준번호 10.1007/978-3-031-27420-6
QR CODE

책소개

전체보기

목차

전체보기

이 주제의 인기대출도서