AIMC Topic: Radiography

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Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey.

Physics in medicine and biology Nov 11, 2022
Reinforcement learning takes sequential decision-making approaches by learning the policy through trial and error based on interaction with the environment. Combining deep learning and reinforcement learning can empower the agent to learn the interac...
Humans Diffusion Magnetic Resonance Imaging Radiography Reinforcement, Psychology Neural Networks, Computer Algorithms
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Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-Rays.

IEEE journal of biomedical and health informatics Nov 10, 2022
Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsibl...
Thoracic Diseases Radiography X-Rays Humans Neural Networks, Computer Tomography, X-Ray Computed
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Prior Guided Transformer for Accurate Radiology Reports Generation.

IEEE journal of biomedical and health informatics Nov 10, 2022
In this paper, we propose a prior guided transformer for accurate radiology reports generation. In the encoder part, a radiograph is firstly represented by a set of patch features, which is obtained through a convolutional neural network and a tradit...
Humans Radiology Normal Distribution Neural Networks, Computer Radiography
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Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.

BMC musculoskeletal disorders Nov 8, 2022
BACKGROUND: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few invest...
Range of Motion, Articular Radiography Cervical Vertebrae Humans Deep Learning Neck
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Natural Language Processing Model for Identifying Critical Findings-A Multi-Institutional Study.

Journal of digital imaging Nov 7, 2022
Improving detection and follow-up of recommendations made in radiology reports is a critical unmet need. The long and unstructured nature of radiology reports limits the ability of clinicians to assimilate the full report and identify all the pertine...
Research Report Retrospective Studies Natural Language Processing Radiography Radiology Humans
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A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?

European journal of radiology Nov 5, 2022
OBJECTIVES: This study aims to contribute to an understanding of the explainability of computer aided diagnosis studies in radiology that use end-to-end deep learning by providing a quantitative overview of methodological choices and by discussing th...
Humans Radiology Deep Learning Radiography Computers Diagnosis, Computer-Assisted
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Introduction to the Veterinary Radiology & Ultrasound Special Issue on Artificial Intelligence.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association Nov 1, 2022
Animals Ultrasonography Artificial Intelligence Radiologists Radiography Radiology
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SAM-X: sorting algorithm for musculoskeletal x-ray radiography.

European radiology Oct 29, 2022
OBJECTIVE: To develop a two-phased deep learning sorting algorithm for post-X-ray image acquisition in order to facilitate large musculoskeletal image datasets according to their anatomical entity.
Deep Learning Algorithms Musculoskeletal Diseases Radiography Retrospective Studies Humans X-Rays
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Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging.

Scientific reports Oct 27, 2022
Thus far, there have been no reported specific rules for systematically determining the appropriate augmented sample size to optimize model performance when conducting data augmentation. In this paper, we report on the feasibility of synthetic data a...
Deep Learning Radiography Automation Humans Algorithms
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Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary Fibrosis.

Radiology Oct 25, 2022
Background Total lung capacity (TLC) has been estimated with use of chest radiographs based on time-consuming methods, such as planimetric techniques and manual measurements. Purpose To develop a deep learning-based, multidimensional model capable of...
Idiopathic Pulmonary Fibrosis Retrospective Studies Humans Aged Radiography Lung Lung Volume Measurements Deep Learning Male Middle Aged
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