AIMC Topic: Diagnostic Imaging

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Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 1: From Methodology to Clinical Implementation.

Journal of the American College of Radiology : JACR
Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. Mo...

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise.

Journal of the American College of Radiology : JACR
Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. Mo...

The rise of deep learning in drug discovery.

Drug discovery today
Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine l...

NiftyNet: a deep-learning platform for medical imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functional...

Machine learning in cardiovascular medicine: are we there yet?

Heart (British Cardiac Society)
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing sev...

Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

Radiology
The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires caref...

Multiscale High-Level Feature Fusion for Histopathological Image Classification.

Computational and mathematical methods in medicine
Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It c...

A mixed-scale dense convolutional neural network for image analysis.

Proceedings of the National Academy of Sciences of the United States of America
Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper ...

Computational Intelligence for Medical Imaging Simulations.

Journal of medical systems
This paper describes how to simulate medical imaging by computational intelligence to explore areas that cannot be easily achieved by traditional ways, including genes and proteins simulations related to cancer development and immunity. This paper ha...

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

Biomedical engineering online
BACKGROUND: Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of...