AIMC Topic: Head and Neck Neoplasms
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A review of deep learning based methods for medical image multi-organ segmentation.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
May 13, 2021
Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical ap...
Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging.
Clinical cancer research : an official journal of the American Association for Cancer Research
May 4, 2021
PURPOSE: Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning-based fully-automated tool called the DeepPET-OPSCC biomarker for predicting ove...
A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
May 4, 2021
BACKGROUND AND PURPOSE: Delineating organs at risk (OARs) on computed tomography (CT) images is an essential step in radiation therapy; however, it is notoriously time-consuming and prone to inter-observer variation. Herein, we report a deep learning...
Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview.
British journal of cancer
Apr 19, 2021
BACKGROUND: This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis.
Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts.
Scientific reports
Mar 19, 2021
Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's...
Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer.
The British journal of radiology
Mar 18, 2021
OBJECTIVES: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetri...
Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning.
Physics in medicine and biology
Mar 16, 2021
Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been propos...
Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study.
International journal of computer assisted radiology and surgery
Mar 5, 2021
PURPOSE: Deep learning (DL) has led to widespread changes in automated segmentation and classification for medical purposes. This study is an attempt to use statistical methods to analyze studies related to segmentation and classification of head and...
An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 24, 2021
INTRODUCTION: Auto contouring models help consistently define volumes and reduce clinical workload. This study aimed to evaluate the cross acquisition of a Magnetic Resonance (MR) deep learning auto contouring model for organ at risk (OAR) delineatio...
Training deep-learning segmentation models from severely limited data.
Medical physics
Feb 19, 2021
PURPOSE: To enable generation of high-quality deep learning segmentation models from severely limited contoured cases (e.g., ~10 cases).
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