BRAX, Brazilian labeled chest x-ray dataset Article Open access 10 August 2022 Introduction The implementation of medical artificial intelligence (AI) into clinical practice in general, and radiology practice in particular, has in large part been limited by the time, cost, and expertise required ...
X-Ray image datasetDeep neural networksRadiographic findingsDifferential diagnosesAnatomical locationsWe present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images ...
In medical imaging, the last decade has witnessed a remarkable increase in the availability and diversity of chest X-ray (CXR) datasets. Concurrently, ther
On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scale high precision computer-aided ...
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
We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San ...
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...
现有的 Chest X-ray 标注困难、成本高,因此需要研究如何利用弱标注和无标注数据 Motivation 对于weakly-labeled data,借鉴 DA for detection 中常用的方法,学习 global classification 对于unlabeled data,借鉴 semi- supervised/DA 中的方法,pseudo labeling
The models are trained on a dataset consisting of 5215 chest X-ray images, containing 1341 images labeled as ‘Normal’, indicating the CXR images have no abnormalities, and 3874 images as ‘Pneumonia’, indicating bacterial or viral pneumonia. Experiments demonstrate the efficacy of our method ...
The Bayes-SqueezeNet was applied for classifying X-ray images labeled in 3 classes as normal, viral pneumonia, and COVID-19. Using the data augmentation, the net claimed to overcome the problem of imbalanced data obtained from the public databases. As another CNN, the CoroNet [11] was ...