AIMC Topic: Radiography, Thoracic

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AI Is Bringing USB Back: Implementing a Beta Chest X-ray Neural Network.

Journal of digital imaging
In a day and age of rapid technological growth and advancement in digital technology, quantum computing, and decentralized cloud computing, it is difficult to get excited about USB sticks, those little dongles that store only a few gigabytes and comm...

Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs.

Journal of digital imaging
Ensuring correct radiograph view labeling is important for machine learning algorithm development and quality control of studies obtained from multiple facilities. The purpose of this study was to develop and test the performance of a deep convolutio...

Generalizable Inter-Institutional Classification of Abnormal Chest Radiographs Using Efficient Convolutional Neural Networks.

Journal of digital imaging
Our objective is to evaluate the effectiveness of efficient convolutional neural networks (CNNs) for abnormality detection in chest radiographs and investigate the generalizability of our models on data from independent sources. We used the National ...

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Journal of digital imaging
Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of ...

Attention-Guided Convolutional Neural Network for Detecting Pneumonia on Chest X-Rays.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pneumonia is a common infectious disease in the world. Its main diagnostic method is chest X-ray (CXR) examination. However, the high visual similarity between a large number of pathologies in CXR makes the interpretation and differentiation of pneum...

Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks. The recent emergence of a large Chest X-ray dataset opened the possi...

Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Respiratory diseases account for a significant proportion of deaths and disabilities across the world. Chest X-ray (CXR) analysis remains a common diagnostic imaging modality for confirming intra-thoracic cardiopulmonary abnormalities. However, there...

Restoration of Full Data from Sparse Data in Low-Dose Chest Digital Tomosynthesis Using Deep Convolutional Neural Networks.

Journal of digital imaging
Chest digital tomosynthesis (CDT) provides more limited image information required for diagnosis when compared to computed tomography. Moreover, the radiation dose received by patients is higher in CDT than in chest radiography. Thus, CDT has not bee...

Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.

Journal of thoracic imaging
Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction using numerous processing layers. A distinctive feature of deep learning, compared with conventional ma...