AIMC Topic: X-Rays

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Algorithmic encoding of protected characteristics in chest X-ray disease detection models.

EBioMedicine
BACKGROUND: It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use this information for making predictions due to undesirable...

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
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...

Improving Anatomical Plausibility in Medical Image Segmentation via Hybrid Graph Neural Networks: Applications to Chest X-Ray Analysis.

IEEE transactions on medical imaging
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...

PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer.

Medical & biological engineering & computing
A long-standing challenge in pneumonia diagnosis is recognizing the pathological lung texture, especially the ground-glass appearance pathological texture. One main difficulty lies in precisely extracting and recognizing the pathological features. Th...

On stars and spikes: Resolving the skeletal morphology of planktonic Acantharia using synchrotron X-ray nanotomography and deep learning image segmentation.

Acta biomaterialia
Acantharia (Acantharea) are wide-spread marine protozoa, presenting one of the rare examples of strontium sulfate mineralization in the biosphere. Their endoskeletons consist of 20 spicules arranged according to a unique geometric pattern named Mülle...

CNN-RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images.

Sensors (Basel, Switzerland)
The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both ...

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays.

Scientific reports
Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians' decision-making is underexplored. In this study, physicians received X-rays with correct diagno...

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...

Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach.

Scientific reports
Chest X-rays are the most economically viable diagnostic imaging test for active pulmonary tuberculosis screening despite the high sensitivity and low specificity when interpreted by clinicians or radiologists. Computer aided detection (CAD) algorith...

X-ray energy spectrum estimation based on a virtual computed tomography system.

Biomedical physics & engineering express
This paper presents a method for estimating the x-ray energy spectrum for computed tomography (CT) in the diagnostic energy range from the reconstructed CT image itself. To this end, a virtual CT system was developed, and datasets, including CT image...