Good news: in the real world of the hospitals, the X-ray is usually labeled in some way, either on the image itself or in the report, especially if it was taken via the portable AP technique.Step 2: Determining image quality In assessing a chest X-ray, there’s a lot to consider,...
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 structu
PadChest has 160,868 chest X-ray images labeled with 174 different radiographic findings, 19 differential diagnoses; only 27% of the labels (totaling 39,053 examples) come from board-certified radiologists, and the rest are obtained by using a recurrent neural network with attention trained on ...
In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significa
2.4 Bounding Box for Pathologies As part of the ChestX-ray8 database, a small number of images with pathology are provided with hand labeled bounding boxes (B-Boxes), which can be used as the ground truth to evaluate the disease localization performance. Fur- thermore...
Imaging is critically important in the diagnosis and management of patients withchest disease, and the chest X-ray remains the most commonly performed imaging examination in medicine. Its correct interpretation is important in not only the diagnosis and exclusion of disease, but also in the choice ...
One particular cause for concern with NLP labels is the issue of systematic or structured mislabeling, where an abnormality is consistently labeled incorrectly in the same way. An example of this occurs in the ChestX-ray14 dataset where subcutaneous emphysema is frequently identified as (pulmonary)...
A fully automated anatomy-directed framework for the segmentation and labeling of the individual bone structures from low-dose chest CT is presented in this paper. The proposed system consists of four main stages: First, both clavicles are segmented and labeled by fitting a piecewise cylindrical ...
models while constructing the weakly-labeled medical im- age database. To tackle these issues, we propose a new chest X-ray database, namely “ChestX-ray8”, which comprises 108,948 frontal-view X-ray images of 32,717 (collected from the year of 1992 to 2015) unique patients with the...
BRAX, Brazilian labeled chest x-ray dataset ArticleOpen access10 August 2022 VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations ArticleOpen access20 July 2022 POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020–2021) ...