AIMC Topic: Observer Variation

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On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment.

IEEE transactions on medical imaging Dec 13, 2019
Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this paper, we propose a method for modelling such uncertainty in the context o...
Humans Neural Networks, Computer Reproducibility of Results Echocardiography Observer Variation Uncertainty
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Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Dec 5, 2019
BACKGROUND: Deep learning-based auto-segmented contours (DC) aim to alleviate labour intensive contouring of organs at risk (OAR) and clinical target volumes (CTV). Most previous DC validation studies have a limited number of expert observers for com...
Deep Learning Head and Neck Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation Humans Male
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Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

Radiation oncology (London, England) Nov 27, 2019
BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subje...
Liver Neoplasms Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Aged Male Middle Aged Deep Learning Observer Variation Radiotherapy Humans Republic of Korea Female Reproducibility of Results Neural Networks, Computer Tomography, X-Ray Computed
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DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs.

Scientific reports Nov 26, 2019
In this study, a deep learning-based method for developing an automated diagnostic support system that detects periodontal bone loss in the panoramic dental radiographs is proposed. The presented method called DeNTNet not only detects lesions but als...
Retrospective Studies Deep Learning Radiography, Panoramic Radiographic Image Interpretation, Computer-Assisted Datasets as Topic Humans Algorithms Observer Variation Dental Hygienists Alveolar Bone Loss
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Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy.

International journal of colorectal disease Nov 6, 2019
INTRODUCTION: Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high-magnification targeting and by the learning curve for image interpret...
Observer Variation Aged, 80 and over Colonic Neoplasms Male Tumor Burden Middle Aged Colonic Polyps Colonoscopy Image Interpretation, Computer-Assisted Machine Learning Microscopy, Confocal Diagnosis, Computer-Assisted Humans Predictive Value of Tests Clinical Competence Female Reproducibility of Results Feasibility Studies Aged
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Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 22, 2019
INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms th...
Humans Neural Networks, Computer Algorithms Female Adult Neoplasm Staging Male Organs at Risk Middle Aged Aged Radiotherapy Planning, Computer-Assisted Young Adult Deep Learning Observer Variation Adolescent Head and Neck Neoplasms Neck
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Endoscopic detection and differentiation of esophageal lesions using a deep neural network.

Gastrointestinal endoscopy Oct 1, 2019
BACKGROUND AND AIMS: Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver variability. Therefore, we developed a computerized image-analysis system to detect and differentia...
Esophageal Squamous Cell Carcinoma Esophageal Diseases Narrow Band Imaging Neural Networks, Computer Esophageal Neoplasms Female Deep Learning Observer Variation Reproducibility of Results Image Processing, Computer-Assisted Precancerous Conditions Aged Neoplasm Invasiveness Optical Imaging Sensitivity and Specificity Esophagus Male Middle Aged Aged, 80 and over Adult Humans
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Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography: Machine Learning for Patient Safety.

JACC. Cardiovascular interventions Sep 25, 2019
OBJECTIVES: This study developed a neural network to perform automated pressure waveform analysis and allow real-time accurate identification of damping.
Middle Aged Humans Europe Patient Safety Arterial Pressure Reproducibility of Results Predictive Value of Tests Neural Networks, Computer Coronary Artery Disease Databases, Factual Aorta Coronary Angiography Risk Factors Heart Rate Machine Learning Female Cardiac Catheterization Aged Artifacts Observer Variation Male
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Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.

Artificial intelligence in medicine Sep 24, 2019
Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early...
Sepsis Male Humans Models, Statistical Middle Aged Observer Variation Machine Learning Diagnosis, Computer-Assisted Intensive Care Units Surveys and Questionnaires Female
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Superior skin cancer classification by the combination of human and artificial intelligence.

European journal of cancer (Oxford, England : 1990) Sep 10, 2019
BACKGROUND: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. Howe...
Deep Learning Dermatologists Image Interpretation, Computer-Assisted Observer Variation Humans Algorithms Prognosis Neural Networks, Computer Skin Neoplasms Dermoscopy
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