AIMC Topic:
Image Interpretation, Computer-Assisted
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Toward a unified framework for interpreting machine-learning models in neuroimaging.
Nature protocols
Mar 18, 2020
Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evalu...
Deep Learning for Automated Sorting of Retinal Photographs.
Ophthalmology. Retina
Mar 17, 2020
PURPOSE: Though the domain of big data and artificial intelligence in health care continues to evolve, there is a lack of systemic methods to improve data quality and streamline the preparation process. To address this, we aimed to develop an automat...
Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm.
Sensors (Basel, Switzerland)
Mar 17, 2020
The detection of objects concealed under people's clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to ...
Sequential classification system for recognition of malaria infection using peripheral blood cell images.
Journal of clinical pathology
Mar 16, 2020
AIMS: Morphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate malaria with respect to other red blood ...
Machine Learning Diagnostic Modeling for Classifying Fibromyalgia Using B-mode Ultrasound Images.
Ultrasonic imaging
Mar 16, 2020
Fibromyalgia (FM) diagnosis remains a challenge for clinicians due to a lack of objective diagnostic tools. One proposed solution is the use of quantitative ultrasound (US) techniques, such as image texture analysis, which has demonstrated discrimina...
A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI.
Magnetic resonance imaging
Mar 12, 2020
BACKGROUND: The classification of benign versus malignant breast lesions on multi-sequence Magnetic Resonance Imaging (MRI) is a challenging task since breast lesions are heterogeneous and complex. Recently, deep learning methods have been used for b...
A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 12, 2020
Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventric...
Prediction of prognosis in patients with tetralogy of Fallot based on deep learning imaging analysis.
Heart (British Cardiac Society)
Mar 11, 2020
OBJECTIVE: To assess the utility of machine learning algorithms for automatically estimating prognosis in patients with repaired tetralogy of Fallot (ToF) using cardiac magnetic resonance (CMR).
Female
Risk Factors
Young Adult
Adult
Humans
Prospective Studies
Prognosis
Deep Learning
Adolescent
Time Factors
Child
Male
Middle Aged
Risk Assessment
Diagnosis, Computer-Assisted
Magnetic Resonance Imaging, Cine
Feasibility Studies
Image Interpretation, Computer-Assisted
Germany
Tetralogy of Fallot
Electrocardiography
Predictive Value of Tests
Registries
AIBx, Artificial Intelligence Model to Risk Stratify Thyroid Nodules.
Thyroid : official journal of the American Thyroid Association
Mar 11, 2020
Current classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivity in medical image interpretation. One out of 2 women over the age of 50 years may have a thyroid n...
Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders.
Sensors (Basel, Switzerland)
Mar 11, 2020
In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the ...
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