AIMC Topic: Diagnostic Imaging

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Classification of Human White Blood Cells Using Machine Learning for Stain-Free Imaging Flow Cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information-rich images of single cells at a throughput of 5,000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several dra...

DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images.

IEEE transactions on medical imaging
Morphology reconstruction of tree-like structures in volumetric images, such as neurons, retinal blood vessels, and bronchi, is of fundamental interest for biomedical research. 3D branch points play an important role in many reconstruction applicatio...

Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging.

Journal of medical imaging and radiation sciences
Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to wea...

A review of medical image detection for cancers in digestive system based on artificial intelligence.

Expert review of medical devices
: At present, cancer imaging examination relies mainly on manual reading of doctors, which requests a high standard of doctors' professional skills, clinical experience, and concentration. However, the increasing amount of medical imaging data has br...

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.

The Lancet. Digital health
BACKGROUND: Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging.

Machine learning for radiomics-based multimodality and multiparametric modeling.

The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of...
Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in onco...

Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
PURPOSE: The required training sample size for a particular machine learning (ML) model applied to medical imaging data is often unknown. The purpose of this study was to provide a descriptive review of current sample-size determination methodologies...

Artificial intelligence in radiology: the ecosystem essential to improving patient care.

Clinical imaging
The rapid development of artificial intelligence (AI) has led to its widespread use in multiple industries, including healthcare. AI has the potential to be a transformative technology that will significantly impact patient care. Particularly, AI has...