AIMC Topic: Diagnostic Imaging

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Model for ASsessing the value of Artificial Intelligence in medical imaging (MAS-AI).

International journal of technology assessment in health care
OBJECTIVES: Artificial intelligence (AI) is seen as a major disrupting force in the future healthcare system. However, the assessment of the value of AI technologies is still unclear. Therefore, a multidisciplinary group of experts and patients devel...

Artificial Intelligence in Lung Imaging.

Seminars in respiratory and critical care medicine
Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging...

Radiomics in neuro-oncological clinical trials.

The Lancet. Digital health
The development of clinical trials has led to substantial improvements in the prevention and treatment of many diseases, including brain cancer. Advances in medicine, such as improved surgical techniques, the development of new drugs and devices, the...

A Review on Multiscale-Deep-Learning Applications.

Sensors (Basel, Switzerland)
In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information...

A Novel Reconstruction Algorithm with High Performance for Compressed Ultrafast Imaging.

Sensors (Basel, Switzerland)
Compressed ultrafast photography (CUP) is a type of two-dimensional (2D) imaging technique to observe ultrafast processes. Intelligence reconstruction methods that influence the imaging quality are an essential part of a CUP system. However, existing...

Superlative Feature Selection Based Image Classification Using Deep Learning in Medical Imaging.

Journal of healthcare engineering
Medical image recognition plays an essential role in the forecasting and early identification of serious diseases in the field of identification. Medical pictures are essential to a patient's health record since they may be used to control, manage, a...

Artificial intelligence in adrenal imaging: A critical review of current applications.

Diagnostic and interventional imaging
In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps f...

Japan Ocular Imaging Registry: a national ophthalmology real-world database.

Japanese journal of ophthalmology
In 2017, the Japanese Ophthalmological Society (JOS) created the Japan Ocular Imaging (JOI) registry, a national database of images and clinical data in the field of ophthalmology in Japan. The JOI registry automatically transfers the information sto...

Text-Guided Human Image Manipulation via Image-Text Shared Space.

IEEE transactions on pattern analysis and machine intelligence
Text is a new way to guide human image manipulation. Albeit natural and flexible, text usually suffers from inaccuracy in spatial description, ambiguity in the description of appearance, and incompleteness. We in this paper address these issues. To o...

SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging.

IEEE journal of biomedical and health informatics
Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, the data from different institutions are usually heterogeneous across institutions, which may reduce...