AIMC Topic: Diagnosis, Computer-Assisted

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Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

Journal of Zhejiang University. Science. B
Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice,...

Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

Investigative radiology
OBJECTIVES: Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radio...

Development of New Diagnostic Techniques - Machine Learning.

Advances in experimental medicine and biology
Traditional diagnoses on addiction reply on the patients' self-reports, which are easy to be dampened by false memory or malingering. Machine learning (ML) is a data-driven procedure that learns algorithms from training data and makes predictions. It...

Horse-Expert: An aided expert system for diagnosing horse diseases.

Polish journal of veterinary sciences
In contrast to the rapid development of the horse husbandry in China, the ability of horse veterinarians to diagnose diseases has not been improved and only a few domain experts have considerable expertise. At present, many expert systems have been d...

Computer aided prognosis for cell death categorization and prediction in vivo using quantitative ultrasound and machine learning techniques.

Medical physics
PURPOSE: At present, a one-size-fits-all approach is typically used for cancer therapy in patients. This is mainly because there is no current imaging-based clinical standard for the early assessment and monitoring of cancer treatment response. Here,...

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.

Medical physics
PURPOSE: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms.