AIMC Topic: Diagnostic Imaging

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The role of artificial intelligence in paediatric neuroradiology.

Pediatric radiology
Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping...

Dual-Path Residual "Shrinkage" Network for Side-Scan Sonar Image Classification.

Computational intelligence and neuroscience
The underwater environment is complicated and changeable and contains many noises, making it difficult to detect a particular object in the underwater environment. At present, the main seabed detection technology explores the seabed environment with ...

All-fiber high-speed image detection enabled by deep learning.

Nature communications
Ultra-high-speed imaging serves as a foundation for modern science. While in biomedicine, optical-fiber-based endoscopy is often required for in vivo applications, the combination of high speed with the fiber endoscopy, which is vital for exploring t...

A Machine-Learning-Based Medical Imaging Fast Recognition of Injury Mechanism for Athletes of Winter Sports.

Frontiers in public health
The Beijing 2022 Winter Olympics will begin soon, which is mainly focused on winter sports. Athletes from different countries will arrive in Beijing one after another for training and competition. The health protection of athletes of winter sports is...

An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension.

Cell reports. Medicine
The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, ...

Quantifying uncertainty in machine learning classifiers for medical imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Machine learning (ML) models in medical imaging (MI) can be of great value in computer aided diagnostic systems, but little attention is given to the confidence (alternatively, uncertainty) of such models, which may have significant clinical...

Deep Correlated Joint Network for 2-D Image-Based 3-D Model Retrieval.

IEEE transactions on cybernetics
In this article, we propose a novel deep correlated joint network (DCJN) approach for 2-D image-based 3-D model retrieval. First, the proposed method can jointly learn two distinct deep neural networks, which are trained for individual modalities to ...

Visual servoing of continuum robots: Methods, challenges, and prospects.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Recent advancements in continuum robotics have accentuated developing efficient and stable controllers to handle shape deformation and compliance. The control of continuum robots (CRs) using physical sensors attached to the robot, particu...

Clinical Explainability Failure (CEF) & Explainability Failure Ratio (EFR) - Changing the Way We Validate Classification Algorithms.

Journal of medical systems
Adoption of Artificial Intelligence (AI) algorithms into the clinical realm will depend on their inherent trustworthiness, which is built not only by robust validation studies but is also deeply linked to the explainability and interpretability of th...

Machine Learning in Cardiovascular Imaging.

Heart failure clinics
The number of cardiovascular imaging studies is growing exponentially, and so is the demand to improve the efficacy of the imaging workflow. Over the past decade, studies have demonstrated that machine learning (ML) holds promise to revolutionize car...