We show that slightly altering a template-based model can increase NLP metrics considerably while maintaining high clinical performance. Our work contributes by a simple but effective approach for chest X-ray report generation, as well as by supporting a model evaluation focused primarily on clinical...
X-ray imagery is significantly more cost effective. However, it does not provide a three-dimensional view of the patient's torso. Hence, this paper describes a technique that, based on a series of geometric template models and two X-ray images of a subject's chest, creates a patient ...
The methodology is applied to the domain of chest radiology, producing a domain-specific lexicon and a series of templates to represent all the relevant clinical information stated on a chest x-ray report. Details about the successive application of the methodology and the resulting versions of ...
We used the saliency package from the People+AI Research (PAIR) group at Google to make heatmaps that show where the models think there are abnormalities in the x-rays. Here's an exampe of a Grad-CAM heatmaps for our binary model's predictions on a single abnormal x-ray. Grad-CAM...
The automatic segmentation of the lung region for chest X-ray (CXR) can help doctors diagnose many lung diseases. However, extreme lung shape changes and fuzzy lung regions caused by serious lung diseases may incorrectly make the automatic lung segmentat
Predicting COVID-19 pneumonia severity on chest X-ray with deep learning. Cureus. 2020;12:e9448. Google Scholar Dembczyński K, Kotłowski W, Słowiński R. Ordinal classification with decision rules. Berlin: Springer Berlin Heidelberg; 2008. p. 169–81. Google Scholar Durán-Rosal AM...
X-rays, CT, MRI, and CT are used to diagnose lung disorders, among which X-Ray is most commonly used for the diagnosis of pneumonia. The proposed architecture will help radiologists to accurately analyze X-rays, CT, MRI, and CT, which could lead to diagnosing other respiratory diseases, ...
Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study...
Existing computer-aided detection schemes for lung nodule detection require a large number of calculations and tens of minutes per case; there is a large gap between image acquisition time and nodule detection time. In this study, we propose a fast detection scheme of lung nodule in chest CT ...
Chung A (2020b) Figure 1 covid-19 chest X-ray data initiative. https://github.com/agchung/Figure1-COVID-chestxray-dataset Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin S...