AI Medical Compendium Topic

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Technical Note: Ontology-guided radiomics analysis workflow (O-RAW).

Medical physics
PURPOSE: Radiomics is the process to automate tumor feature extraction from medical images. This has shown potential for quantifying the tumor phenotype and predicting treatment response. The three major challenges of radiomics research and clinical ...

ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning.

Scientific reports
Machine learning algorithms hold the promise to effectively automate the analysis of histopathological images that are routinely generated in clinical practice. Any machine learning method used in the clinical diagnostic process has to be extremely a...

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scal...

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.

The Journal of pathology
In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated pa...

Measurement-oriented deep-learning workflow for improved segmentation of myelin and axons in high-resolution images of human cerebral white matter.

Journal of neuroscience methods
BACKGROUND: Standard segmentation of high-contrast electron micrographs (EM) identifies myelin accurately but does not translate easily into measurements of individual axons and their myelin, even in cross-sections of parallel fibers. We describe aut...

ROBOT: A Tool for Automating Ontology Workflows.

BMC bioinformatics
BACKGROUND: Ontologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and appl...

Chemical-induced disease relation extraction via attention-based distant supervision.

BMC bioinformatics
BACKGROUND: Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedic...

Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice.

The Journal of pathology
The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a...