AIMC Topic: Observer Variation

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Cancer taxonomy: pathology beyond pathology.

European journal of cancer (Oxford, England : 1990) May 17, 2019
The way we categorise and classify cancer types dictates not only the way we diagnose and treat patients but also many of our decisions on biomarker and drug development. In addition, cancer taxonomy proves the ground truth for future discoveries in ...
Humans Predictive Value of Tests Biopsy Pathologists Microscopy Artificial Intelligence Reproducibility of Results Neoplasms Diagnosis, Computer-Assisted Image Interpretation, Computer-Assisted Observer Variation Classification
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Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.

Virchows Archiv : an international journal of pathology May 16, 2019
Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate b...
Humans Automation, Laboratory Reproducibility of Results Male Biopsy Image Interpretation, Computer-Assisted Observer Variation Pattern Recognition, Automated Deep Learning Predictive Value of Tests Prostatic Neoplasms Neoplasm Grading
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A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.

JACC. Cardiovascular imaging May 15, 2019
OBJECTIVES: This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional ...
Predictive Value of Tests Myocardial Infarction Image Interpretation, Computer-Assisted Observer Variation Aged Echocardiography Humans Aged, 80 and over Middle Aged Male Retrospective Studies Diagnosis, Computer-Assisted Ventricular Function, Left Deep Learning Female Reproducibility of Results
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A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.

Medical physics Apr 1, 2019
PURPOSE: This work aims to develop a new framework of image quality assessment using deep learning-based model observer (DL-MO) and to validate it in a low-contrast lesion detection task that involves CT images with patient anatomical background.
Radiotherapy Planning, Computer-Assisted Observer Variation Radiotherapy Dosage Image Processing, Computer-Assisted Models, Statistical Least-Squares Analysis Humans Neural Networks, Computer Algorithms Retrospective Studies Tomography, X-Ray Computed Deep Learning Liver Neoplasms
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Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

European radiology Mar 29, 2019
OBJECTIVES: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).
Humans Ultrasonography, Mammary Image Interpretation, Computer-Assisted Neural Networks, Computer Algorithms Observer Variation Retrospective Studies Adult Breast Neoplasms Middle Aged Clinical Decision-Making ROC Curve Female Aged
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A deep neural network improves endoscopic detection of early gastric cancer without blind spots.

Endoscopy Mar 12, 2019
BACKGROUND: Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early g...
Humans Neural Networks, Computer Stomach Neoplasms Sensitivity and Specificity Clinical Competence Gastroscopy Early Detection of Cancer Diagnosis, Differential Observer Variation
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Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.

Medical physics Mar 12, 2019
OBJECTIVES: The aim of this study was to develop a fully automated deep learning approach for identification of the pectoral muscle on mediolateral oblique (MLO) view mammograms and evaluate its performance in comparison to our previously developed t...
Pectoralis Muscles Humans Image Processing, Computer-Assisted Neural Networks, Computer Mammography Algorithms Radiologists Female Observer Variation Breast Neoplasms Diagnosis, Computer-Assisted Deep Learning
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Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach.

International journal of computer assisted radiology and surgery Mar 7, 2019
PURPOSE: In radiation therapy, a key step for a successful cancer treatment is image-based treatment planning. One objective of the planning phase is the fast and accurate segmentation of organs at risk and target structures from medical images. Howe...
Deep Learning Imaging, Three-Dimensional Head and Neck Neoplasms Observer Variation Humans Neural Networks, Computer Tomography, X-Ray Computed
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Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis-Experience from the LSOS study cohort.

European journal of radiology Feb 19, 2019
PURPOSE: To investigate and compare the reproducibility and accuracy of qualitative ratings and quantitative texture analysis (TA) in detection and grading of lumbar spinal stenosis (LSS) in magnetic resonance imaging (MR) scans of the lumbar spine.
Reference Standards Male Female Humans Reproducibility of Results Middle Aged Machine Learning Aged Cohort Studies Algorithms Aged, 80 and over Magnetic Resonance Imaging Lumbar Vertebrae Spinal Stenosis Outcome Assessment, Health Care Observer Variation
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Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.

Medical physics Feb 19, 2019
PURPOSE: Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other ...
Algorithms Male Imaging, Three-Dimensional Image Processing, Computer-Assisted Humans Neural Networks, Computer Magnetic Resonance Imaging Prostate Observer Variation Automation
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