AIMC Topic: Thorax

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FasterRib: A deep learning algorithm to automate identification and characterization of rib fractures on chest computed tomography scans.

The journal of trauma and acute care surgery Mar 6, 2023
OBJECTIVE: Characterizing and enumerating rib fractures are critical to informing clinical decisions, yet in-depth characterization is rarely performed because of the manual burden of annotating these injuries on computed tomography (CT) scans. We hy...
Neural Networks, Computer Rib Fractures Humans Deep Learning Thorax Tomography, X-Ray Computed Retrospective Studies
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Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss.

Computers in biology and medicine Feb 15, 2023
-Thoracic disease, like many other diseases, can lead to complications. Existing multi-label medical image learning problems typically include rich pathological information, such as images, attributes, and labels, which are crucial for supplementary ...
X-Rays Thorax Deep Learning Semantics Image Processing, Computer-Assisted
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CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia From Chest X-Ray Images.

IEEE journal of biomedical and health informatics Feb 3, 2023
Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learn...
Thorax COVID-19 Testing COVID-19 Deep Learning Pneumonia, Viral Humans X-Rays
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Improving Anatomical Plausibility in Medical Image Segmentation via Hybrid Graph Neural Networks: Applications to Chest X-Ray Analysis.

IEEE transactions on medical imaging Feb 2, 2023
Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These models are often trained with loss functions such as cross-entropy or D...
Neural Networks, Computer Thorax X-Rays Radiography Image Processing, Computer-Assisted
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COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods.

International journal of environmental research and public health Jan 22, 2023
Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and prev...
X-Rays Thorax Deep Learning COVID-19 Testing Humans COVID-19
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A deep learning based dual encoder-decoder framework for anatomical structure segmentation in chest X-ray images.

Scientific reports Jan 16, 2023
Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct str...
Deep Learning Neural Networks, Computer Thorax Image Processing, Computer-Assisted Humans X-Rays
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Centralized contrastive loss with weakly supervised progressive feature extraction for fine-grained common thorax disease retrieval in chest x-ray.

Medical physics Jan 11, 2023
BACKGROUND: Medical images have already become an essential tool for the diagnosis of many diseases. Thus a large number of medical images are being generated due to the daily routine inspection. An efficient image-based disease retrieval system will...
Thorax Neural Networks, Computer X-Rays Radiography Algorithms
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Current Advances in Computational Lung Ultrasound Imaging: A Review.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Jan 11, 2023
In the field of biomedical imaging, ultrasonography has become common practice, and used as an important auxiliary diagnostic tool with unique advantages, such as being non-ionizing and often portable. This article reviews the state-of-the-art in med...
Thorax Ultrasonography Image Processing, Computer-Assisted Lung Machine Learning
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Medical Image Classification Based on Semi-Supervised Generative Adversarial Network and Pseudo-Labelling.

Sensors (Basel, Switzerland) Dec 17, 2022
Deep learning has substantially improved the state-of-the-art in object detection and image classification. Deep learning usually requires large-scale labelled datasets to train the models; however, due to the restrictions in medical data sharing and...
Databases, Factual Information Dissemination Supervised Machine Learning Thorax
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Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation.

Scientific reports Nov 25, 2022
Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules ne...
Deep Learning Tomography, X-Ray Computed Thorax Neural Networks, Computer Lung
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