AIMC Topic: X-Rays

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Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system.

Scientific reports Oct 16, 2023
To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following...
Tomography, X-Ray Computed Deep Learning Computers Retrospective Studies Radiographic Image Interpretation, Computer-Assisted COVID-19 Testing X-Rays Pneumonia Radiologists Humans COVID-19
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A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images.

Scientific reports Oct 16, 2023
We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without a...
Humans Algorithms Imaging, Three-Dimensional X-Rays Image Processing, Computer-Assisted Constriction, Pathologic Coronary Angiography Coronary Vessels Neural Networks, Computer
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Volumetric tumor tracking from a single cone-beam X-ray projection image enabled by deep learning.

Medical image analysis Oct 10, 2023
Radiotherapy serves as a pivotal treatment modality for malignant tumors. However, the accuracy of radiotherapy is significantly compromised due to respiratory-induced fluctuations in the size, shape, and position of the tumor. To address this challe...
Cone-Beam Computed Tomography Imaging, Three-Dimensional X-Rays Neoplasms Deep Learning Humans Tomography, X-Ray Computed
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AC-Faster R-CNN: an improved detection architecture with high precision and sensitivity for abnormality in spine x-ray images.

Physics in medicine and biology Sep 26, 2023
In clinical medicine, localization and identification of disease on spinal radiographs are difficult and require a high level of expertise in the radiological discipline and extensive clinical experience. The model based on deep learning acquires cer...
X-Rays Neural Networks, Computer Radiography Radiology Algorithms
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Real-time liver motion estimation via deep learning-based angle-agnostic X-ray imaging.

Medical physics Sep 13, 2023
BACKGROUND: Real-time liver imaging is challenged by the short imaging time (within hundreds of milliseconds) to meet the temporal constraint posted by rapid patient breathing, resulting in extreme under-sampling for desired 3D imaging. Deep learning...
X-Rays Humans Motion Deep Learning Phantoms, Imaging Liver Neoplasms
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Multi-Label Local to Global Learning: A Novel Learning Paradigm for Chest X-Ray Abnormality Classification.

IEEE journal of biomedical and health informatics Sep 6, 2023
Deep neural network (DNN) approaches have shown remarkable progress in automatic Chest X-rays classification. However, existing methods use a training scheme that simultaneously trains all abnormalities without considering their learning priority. In...
Humans Neural Networks, Computer Datasets as Topic Radiography, Thoracic X-Rays Machine Learning Models, Biological
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Image quality improvement in bowtie-filter-equipped cone-beam CT using a dual-domain neural network.

Medical physics Sep 5, 2023
BACKGROUND: The bowtie-filter in cone-beam CT (CBCT) causes spatially nonuniform x-ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient-depende...
Phantoms, Imaging X-Rays Cone-Beam Computed Tomography Image Processing, Computer-Assisted Quality Improvement Artifacts Neural Networks, Computer Algorithms Humans
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Improving chest X-ray report generation by leveraging warm starting.

Artificial intelligence in medicine Aug 19, 2023
Automatically generating a report from a patient's Chest X-rays (CXRs) is a promising solution to reducing clinical workload and improving patient care. However, current CXR report generators-which are predominantly encoder-to-decoder models-lack the...
Workload Humans X-Rays Natural Language Processing
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Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation.

European radiology Aug 12, 2023
OBJECTIVES: To assess whether a computer-aided detection (CADe) system could serve as a learning tool for radiology residents in chest X-ray (CXR) interpretation.
Humans X-Rays Artificial Intelligence Internship and Residency Radiography Radiography, Thoracic
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Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets.

Scientific reports Aug 4, 2023
Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding ...
Humans Deep Learning Radiography, Thoracic Radiographic Image Interpretation, Computer-Assisted COVID-19 X-Rays
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