Artificial intelligence for point of care radiograph quality assessment
Chest X-rays are among the most common modalities in medical imaging. Technical flaws of
these images, such as over-or under-exposure or wrong positioning of the patients can …
these images, such as over-or under-exposure or wrong positioning of the patients can …
Identifying disease-free chest x-ray images with deep transfer learning
Chest X-rays (CXRs) are among the most commonly used medical image modalities. They
are mostly used for screening, and an indication of disease typically results in subsequent …
are mostly used for screening, and an indication of disease typically results in subsequent …
Can artificial intelligence reliably report chest x-rays?: Radiologist validation of an algorithm trained on 2.3 million x-rays
P Putha, M Tadepalli, B Reddy, T Raj… - arXiv preprint arXiv …, 2018 - arxiv.org
Background: Chest X-rays are the most commonly performed, cost-effective diagnostic
imaging tests ordered by physicians. A clinically validated AI system that can reliably …
imaging tests ordered by physicians. A clinically validated AI system that can reliably …
A semi‐supervised learning‐based quality evaluation system for digital chest radiographs
S Wei, R Qiu, Y Pu, A Hu, Y Niu, Z Wu… - Medical …, 2023 - Wiley Online Library
Background Digital radiography is the most commonly utilized medical imaging technique
worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis …
worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis …
Evaluating the implementation of deep learning in librehealth radiology on chest x-rays
S Purkayastha, SB Buddi, S Nuthakki, B Yadav… - Advances in Computer …, 2020 - Springer
Respiratory diseases are the dominant cause of deaths worldwide. In the US, the number of
deaths due to chronic lung infections (mostly pneumonia and tuberculosis), lung cancer and …
deaths due to chronic lung infections (mostly pneumonia and tuberculosis), lung cancer and …
[HTML][HTML] Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Background Chest radiograph interpretation is critical for the detection of thoracic diseases,
including tuberculosis and lung cancer, which affect millions of people worldwide each year …
including tuberculosis and lung cancer, which affect millions of people worldwide each year …
Deep learning for triage of chest radiographs: should every institution train its own system?
B van Ginneken - Radiology, 2019 - pubs.rsna.org
Deep Learning for Triage of Chest Radiographs 546 radiology. rsna. org n Radiology:
Volume 290: Number 2—February 2019 reasonably well (AUC= 0.93), but its performance …
Volume 290: Number 2—February 2019 reasonably well (AUC= 0.93), but its performance …
[HTML][HTML] High-throughput classification of radiographs using deep convolutional neural networks
The study aimed to determine if computer vision techniques rooted in deep learning can use
a small set of radiographs to perform clinically relevant image classification with high fidelity …
a small set of radiographs to perform clinically relevant image classification with high fidelity …
Automating chest radiograph imaging quality control
K Nousiainen, T Mäkelä, A Piilonen, JI Peltonen - Physica Medica, 2021 - Elsevier
Purpose To automate diagnostic chest radiograph imaging quality control (lung inclusion at
all four edges, patient rotation, and correct inspiration) using convolutional neural network …
all four edges, patient rotation, and correct inspiration) using convolutional neural network …
Chest X-ray diagnostic quality assessment: How much is pixel-wise supervision needed?
Chest X-ray is an important imaging method for the diagnosis of chest diseases. Chest
radiograph diagnostic quality assessment is vital for the diagnosis of the disease because …
radiograph diagnostic quality assessment is vital for the diagnosis of the disease because …