Head And Neck Tumor Segmentation And Outcome Prediction
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Head And Neck Tumor Segmentation And Outcome Prediction
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Author : Vincent Andrearczyk
language : en
Publisher: Springer Nature
Release Date : 2022-03-12
Head And Neck Tumor Segmentation And Outcome Prediction written by Vincent Andrearczyk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-12 with Computers categories.
This book constitutes the Second 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic. The 29 contributions presented, as well as an overview paper, were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 325 delineated PET/CT images was made available for training.
Head And Neck Tumor Segmentation And Outcome Prediction
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Author : Vincent Andrearczyk
language : en
Publisher: Springer Nature
Release Date : 2023-03-17
Head And Neck Tumor Segmentation And Outcome Prediction written by Vincent Andrearczyk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-17 with Computers categories.
This book constitutes the Third 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, on September 22, 2022. The 22 contributions presented, as well as an overview paper, were carefully reviewed and selected from 24 submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 883 delineated PET/CT images was made available for training.
Head And Neck Tumor Segmentation
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Author : Vincent Andrearczyk
language : en
Publisher: Springer Nature
Release Date : 2021-01-12
Head And Neck Tumor Segmentation written by Vincent Andrearczyk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-12 with Computers categories.
This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 2 full and 8 short papers presented together with an overview paper in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results.
Outcome Prediction In Head And Neck Cancer Patients Using Machine Learning Methods
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Author : David John Dellsperger
language : en
Publisher:
Release Date : 2014
Outcome Prediction In Head And Neck Cancer Patients Using Machine Learning Methods written by David John Dellsperger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Head categories.
Head and Neck cancers account for approximately 3.2% of the estimated 1,660,290 new cancer cases for the year 2013 and roughly 1.9% of cancer-related deaths in 2013. In this research, machine learning techniques were employed to predict outcome in cancer patients supporting more objective assessment of the treatments, including surgery, radiation therapy, or chemotherapy. Selection of features capable of distinguishing between the possible outcomes was accomplished by using a highly selective cohort of 61 patients with similar treatment and location of the primary tumor. An accuracy of 80.33% (compared to a baseline majority classifier of 60.66%) was achieved utilizing this cohort. Further, it is shown that this limited cohort has the power to provide valuable information on outcome prediction utilizing as few as four features. Feature selection was drawn from both clinical features and quantitative imaging features including the site of cancer, primary tumor volume, and race.
Deep Learning For Head And Neck Tumor Segmentation
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Author :
language : en
Publisher:
Release Date :
Deep Learning For Head And Neck Tumor Segmentation written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Medical Image Computing And Computer Assisted Intervention Miccai 2023
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Author : Hayit Greenspan
language : en
Publisher: Springer Nature
Release Date : 2023-09-30
Medical Image Computing And Computer Assisted Intervention Miccai 2023 written by Hayit Greenspan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-30 with Computers categories.
The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
Artificial Intelligence In Healthcare
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Author : Xianghua Xie
language : en
Publisher: Springer Nature
Release Date :
Artificial Intelligence In Healthcare written by Xianghua Xie and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Methods In Cancer Imaging And Image Directed Interventions
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Author : Bahram Mohajer
language : en
Publisher: Frontiers Media SA
Release Date : 2023-11-08
Methods In Cancer Imaging And Image Directed Interventions written by Bahram Mohajer and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-08 with Medical categories.
Frontiers in Oncology is delighted to present the Methods in series of article collections. Methods in Cancer Imaging and Image-directed Interventions will publish high-quality methodical studies on key topics in the field. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries. The Methods in Cancer Imaging and Image-directed Interventions collection aims to highlight the latest experimental techniques and methods used to investigate fundamental questions in Cancer Imaging and Image-directed Interventions. Review Articles or Opinion Articles on methodologies or applications including the advantages and limitations of each are welcome. This Research Topic includes technologies and up-to-date methods which help aim to help advance science.
Explainable And Transparent Ai And Multi Agent Systems
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Author : Davide Calvaresi
language : en
Publisher: Springer Nature
Release Date :
Explainable And Transparent Ai And Multi Agent Systems written by Davide Calvaresi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Medical Image Computing And Computer Assisted Intervention Miccai 2022
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Author : Linwei Wang
language : en
Publisher: Springer Nature
Release Date : 2022-09-16
Medical Image Computing And Computer Assisted Intervention Miccai 2022 written by Linwei Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-16 with Computers categories.
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.