AIMC Topic: Diagnostic Imaging

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TIE-GANs: single-shot quantitative phase imaging using transport of intensity equation with integration of GANs.

Journal of biomedical optics
SIGNIFICANCE: Artificial intelligence (AI) has become a prominent technology in computational imaging over the past decade. The expeditious and label-free characteristics of quantitative phase imaging (QPI) render it a promising contender for AI inve...

A scoping review of educational programmes on artificial intelligence (AI) available to medical imaging staff.

Radiography (London, England : 1995)
INTRODUCTION: Medical imaging is arguably the most technologically advanced field in healthcare, encompassing a range of technologies which continually evolve as computing power and human knowledge expand. Artificial Intelligence (AI) is the next fro...

MAPS: pathologist-level cell type annotation from tissue images through machine learning.

Nature communications
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resour...

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives.

Computers in biology and medicine
Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortu...

Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging Informatics.

Yearbook of medical informatics
OBJECTIVES: This review presents research papers highlighting notable developments and trends in sensors, signals, and imaging informatics (SSII) in 2022.

Restoration of metabolic functional metrics from label-free, two-photon human tissue images using multiscale deep-learning-based denoising algorithms.

Journal of biomedical optics
SIGNIFICANCE: Label-free, two-photon excited fluorescence (TPEF) imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, noise and other artifacts present in these image...

Training Universal Deep-Learning Networks for Electromagnetic Medical Imaging Using a Large Database of Randomized Objects.

Sensors (Basel, Switzerland)
Deep learning has become a powerful tool for solving inverse problems in electromagnetic medical imaging. However, contemporary deep-learning-based approaches are susceptible to inaccuracies stemming from inadequate training datasets, primarily consi...

A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence.

Arthritis care & research
The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is u...

Deployment and assessment of a deep learning model for real-time detection of anal precancer with high frame rate high-resolution microendoscopy.

Scientific reports
Anal cancer incidence is significantly higher in people living with HIV as HIV increases the oncogenic potential of human papillomavirus. The incidence of anal cancer in the United States has recently increased, with diagnosis and treatment hampered ...