AI Medical Compendium Topic

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Thorax

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Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images.

Canadian respiratory journal
BACKGROUND AND AIMS: Chest X-ray (CXR) is indispensable to the assessment of severity, diagnosis, and management of pneumonia. Deep learning is an artificial intelligence (AI) technology that has been applied to the interpretation of medical images. ...

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation.

Scientific reports
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...

Medical Image Classification Based on Semi-Supervised Generative Adversarial Network and Pseudo-Labelling.

Sensors (Basel, Switzerland)
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...

COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods.

International journal of environmental research and public health
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...

A deep learning based dual encoder-decoder framework for anatomical structure segmentation in chest X-ray images.

Scientific reports
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...

Centralized contrastive loss with weakly supervised progressive feature extraction for fine-grained common thorax disease retrieval in chest x-ray.

Medical physics
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...

Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss.

Computers in biology and medicine
-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 ...

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account...

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
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...

Framework for dual-energy-like chest radiography image synthesis from single-energy computed tomography based on cycle-consistent generative adversarial network.

Medical physics
BACKGROUND: Dual-energy (DE) chest radiography (CXR) enables the selective imaging of two relevant materials, namely, soft tissue and bone structures, to better characterize various chest pathologies (i.e., lung nodule, bony lesions, etc.) and potent...