AI Medical Compendium Topic

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A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection.

International journal of computer assisted radiology and surgery
PURPOSE: Although a novel deep learning software was proposed using post-processed images obtained by the fusion between X-ray images of normal post-operative radiography and surgical sponge, the association of the retained surgical item detectabilit...

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.

Journal of applied clinical medical physics
PURPOSE: The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment.

Artificial Intelligence in Breast X-Ray Imaging.

Seminars in ultrasound, CT, and MR
This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The range of AI applications is broad. AI can be used for breast cancer risk estimation that could allow f...

X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels.

Computers in biology and medicine
In clinical applications, multi-dose scan protocols will cause the noise levels of computed tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising network outputs denoised images through an end-to-end mapping between an ...

Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN.

Sensors (Basel, Switzerland)
We propose a new generative model named adaptive cycle-consistent generative adversarial network, or Ad CycleGAN to perform image translation between normal and COVID-19 positive chest X-ray images. An independent pre-trained criterion is added to th...

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray.

IEEE journal of biomedical and health informatics
Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However,...

Chest X-ray-based opportunistic screening of sarcopenia using deep learning.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Early detection and management of sarcopenia is of clinical importance. We aimed to develop a chest X-ray-based deep learning model to predict presence of sarcopenia.

Dead detector element detection in flat panels using convolutional neural networks.

Medical physics
BACKGROUND: Independent testing of image quality metrics is important to provide an unbiased determination of medical imaging performance. Due to the underreporting by vendors of dead detector elements, which are elements that do not function but may...

X-ray dose profiles using artificial neural networks.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
This paper introduces a novel computational method to simulate and predict radiation dose profiles in a water phantom irradiated by X-rays of 6 and 15 MV at different depths and field sizes using Artificial Neural Networks within the error margin req...

A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques.

Network (Bristol, England)
COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead t...