AIMC Topic:
Image Interpretation, Computer-Assisted

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Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections.

PloS one May 20, 2019
PURPOSE: To compare manual corrections of liver masks produced by a fully automatic segmentation method based on convolutional neural networks (CNN) with manual routine segmentations in MR images in terms of inter-observer variability and interaction...
Humans Observer Variation Neural Networks, Computer Algorithms Liver Neoplasms Carcinoma, Hepatocellular Magnetic Resonance Imaging Liver Image Interpretation, Computer-Assisted
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Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

The international journal of cardiovascular imaging May 18, 2019
Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to find out novel analytical approaches to improve pre-implantation CRT response prediction. Among 31 pre-...
Ventricular Remodeling Image Interpretation, Computer-Assisted Humans Signal Processing, Computer-Assisted Patient Selection Heart Failure Clinical Decision-Making Middle Aged Cardiac Resynchronization Therapy Female Electrocardiography Aged, 80 and over Aged Treatment Outcome Male Support Vector Machine Predictive Value of Tests Ventricular Function, Left Echocardiography
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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 ...
Microscopy Humans Image Interpretation, Computer-Assisted Predictive Value of Tests Observer Variation Neoplasms Artificial Intelligence Classification Biopsy Diagnosis, Computer-Assisted Reproducibility of Results Pathologists
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Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis.

BMC cancer May 17, 2019
PURPOSE: To explore imaging biomarkers that can be used for diagnosis and prediction of pathologic stage in non-small cell lung cancer (NSCLC) using multiple machine learning algorithms based on CT image feature analysis.
Male Lung Neoplasms Carcinoma, Non-Small-Cell Lung Neoplasm Staging Humans Machine Learning Tomography, X-Ray Computed Female Image Interpretation, Computer-Assisted
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Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer's Disease Based on an Extreme Learning Machine Method from the ADNI cohort.

Neuroscience May 16, 2019
Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate between patients with AD and normal controls (NCs) usi...
Female Aged Aged, 80 and over Male Humans Alzheimer Disease Support Vector Machine Machine Learning Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Brain Neuroimaging
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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...
Male Deep Learning Predictive Value of Tests Prostatic Neoplasms Neoplasm Grading Humans Automation, Laboratory Reproducibility of Results Biopsy Image Interpretation, Computer-Assisted Observer Variation Pattern Recognition, Automated
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Impact of pre-analytical variables on deep learning accuracy in histopathology.

Histopathology May 16, 2019
AIMS: Machine learning (ML) binary classification in diagnostic histopathology is an area of intense investigation. Several assumptions, including training image quality/format and the number of training images required, appear to be similar in many ...
Humans Neural Networks, Computer Deep Learning Machine Learning Image Interpretation, Computer-Assisted Female Male Diagnosis, Computer-Assisted Histological Techniques Pathology, Clinical Histology
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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 ...
Female Reproducibility of Results Aged Aged, 80 and over Echocardiography Middle Aged Diagnosis, Computer-Assisted Male Ventricular Function, Left Humans Retrospective Studies Observer Variation Deep Learning Predictive Value of Tests Myocardial Infarction Image Interpretation, Computer-Assisted
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Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa.

Magnetic resonance imaging May 15, 2019
PURPOSE: To evaluate whether a machine learning (ML) analysis employing MRI-derived texture analysis (TA) features could be useful in assessing the presence of placenta accreta spectrum (PAS) in patients with placenta previa (PP). The hypothesis is t...
Sensitivity and Specificity Magnetic Resonance Imaging Predictive Value of Tests Bayes Theorem Prenatal Diagnosis Placenta Accreta Placenta Previa Placenta Image Interpretation, Computer-Assisted Pregnancy Female Reproducibility of Results Adult Humans Machine Learning Algorithms Retrospective Studies
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Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.

European radiology May 15, 2019
OBJECTIVES: To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier.
Proof of Concept Study Cholangiocarcinoma Bile Ducts, Intrahepatic Magnetic Resonance Imaging Predictive Value of Tests Image Interpretation, Computer-Assisted Retrospective Studies Bile Duct Neoplasms Female Aged Adult Deep Learning Humans Liver Neoplasms Neural Networks, Computer Carcinoma, Hepatocellular Machine Learning Male Algorithms Middle Aged
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