We additionally show that, by fine-tuning the original model using the automatically labelled exams for retraining, performance can be preserved or improved, resulting in a highly accurate, more generalized model.Similar content being viewed by others CLARE-XR: explainable regression-based classification...
The anteroposterior X-ray shows a slight increase in subpleural ground-glass density in the middle-lower third of the right hemithorax (arrow), probably defined medially by the major fissure. No SARS-CoV-2 PCR or other microbiological determinations were performed; however, it was labelled as ...
We apply the Progressive Growing GAN (PGGAN) to the task of chest x-ray generation with the goal of being able to produce images without any ethical concerns that may be used for medical education or in other machine learning work. We evaluate the properties of the generated x-rays with ...
As for chest x-ray findings by survey physicians, a smaller proportion of bacteriologically positive chest x-rays were labelled “normal” (7% [18/258] vs 37% [191/516]), and a greater proportion were graded “abnormal suggestive of tuberculosis” (93% [240/258] vs 61% [317/516]). ...
We leverage some of the advanced ConvNet architectures as a backbone-model of the proposed attention mapping network to build Cardio-XAttentionNet. The proposed model is trained on ChestX-Ray14, which is a publicly accessible chest X-ray dataset. The best single model achieves an overall ...
Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., ”COVID-19”, ”Pneumonia”, and ”Normal”. In order to capture the quality of resampling methods, 10-folds cross ...
The application of deep learning to chest X- ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-...
Rajaraman S, Antani S (2020) Training deep learning algorithms with weakly labelled pneumonia chest X-ray data for COVID-19 detection. medRxiv preprint. https://doi.org/10.1101/2020.05.04.20090803 Ranjan R, Sharma A, Verma MK (2021) Characterization of the second wave of COVID-19 in India...
Chest X-rayComputer aided diagnosisChest X-rays (CXRs) are a crucial and extraordinarily common diagnostic tool, leading to heavy research for computer-aided diagnosis (CAD) solutions. However, both high classification accuracy and meaningful model predictions that respect and incorporate clinical ...
Of the 14 observations labelled in the CheXpert dataset, ‘fracture’ and ‘pleural other’ were not included in our analysis because they had low prevalence in our test set (fewer than ten examples), ‘pneumonia’ was not included because it is a clinical (as opposed to a radiological) ...