AIMC Topic: Diagnostic Imaging
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Artificial Intelligence in Radiology Residency Training.
Seminars in musculoskeletal radiology
Jan 28, 2020
Artificial intelligence (AI) is an emerging technology that brings a wide array of new tools to the field of radiology. AI will certainly have an impact on the day-to-day work of radiologists in the coming decades, thus training programs must prepare...
From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value.
Seminars in musculoskeletal radiology
Jan 28, 2020
The radiology practice has access to a wealth of data in the radiologist information system, dictation reports, and electronic health records. Although many artificial intelligence applications in radiology have focused on computer vision and the int...
Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics.
Seminars in musculoskeletal radiology
Jan 28, 2020
Although still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction...
Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence.
Seminars in musculoskeletal radiology
Jan 28, 2020
Artificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect a...
Artificial Intelligence Explained for Nonexperts.
Seminars in musculoskeletal radiology
Jan 28, 2020
Artificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or t...
Musculoskeletal Imaging Applications of Artificial Intelligence.
Seminars in musculoskeletal radiology
Jan 28, 2020
Deep Learning-Based Single-Cell Optical Image Studies.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Jan 25, 2020
Optical imaging technology that has the advantages of high sensitivity and cost-effectiveness greatly promotes the progress of nondestructive single-cell studies. Complex cellular image analysis tasks such as three-dimensional reconstruction call for...
Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images.
Communications biology
Jan 8, 2020
Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachmen...
Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study.
Chest
Dec 28, 2019
COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year lo...
Survey on deep learning for pulmonary medical imaging.
Frontiers of medicine
Dec 16, 2019
As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and d...
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