AIMC Topic: Machine Learning

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Canadian Association of Radiologists White Paper on De-identification of Medical Imaging: Part 2, Practical Considerations.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboratio...

Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboratio...

Interpretation of cluster structures in pain-related phenotype data using explainable artificial intelligence (XAI).

European journal of pain (London, England)
BACKGROUND: In pain research and clinics, it is common practice to subgroup subjects according to shared pain characteristics. This is often achieved by computer-aided clustering. In response to a recent EU recommendation that computer-aided decision...

Application of machine learning to identify predators of stocked fish in Lake Ontario: using acoustic telemetry predation tags to inform management.

Journal of fish biology
Understanding predator-prey interactions and food web dynamics is important for ecosystem-based management in aquatic environments, as they experience increasing rates of human-induced changes, such as the addition and removal of fishes. To quantify ...

Artificial intelligence in endoscopy: Present and future perspectives.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Artificial intelligence (AI) has been attracting considerable attention as an important scientific topic in the field of medicine. Deep-leaning (DL) technologies have been applied more dominantly than other traditional machine-learning methods. They ...

The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs.

Advances in health sciences education : theory and practice
Data science is an inter-disciplinary field that uses computer-based algorithms and methods to gain insights from large and often complex datasets. Data science, which includes Artificial Intelligence techniques such as Machine Learning (ML), has bee...

Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning.

IEEE transactions on pattern analysis and machine intelligence
In this paper, the problem of describing visual contents of a video sequence with natural language is addressed. Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstru...

Gravitational Laws of Focus of Attention.

IEEE transactions on pattern analysis and machine intelligence
The understanding of the mechanisms behind focus of attention in a visual scene is a problem of great interest in visual perception and computer vision. In this paper, we describe a model of scanpath as a dynamic process which can be interpreted as a...

Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.

Journal of the American Heart Association
Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community-based interventions have been shown to be both effective and cost-effective in preventing cardiovascul...