Radiographs of the heart and lungs also capture parts of the liver, allowing for deep learning models to detect fatty liver ...

Our data shows growing demand for portable xray machines as clinics, hospitals, and home-care teams hunt for quicker scans ...

Features Multi-label classification for up to 6 thoracic diseases Multiple model architectures: ResNet18, EfficientNetB0/B2, MobileNet, Swin Transformer Preprocessing with normalization and data ...

Keywords: dynamic chest X-ray images, lung field segmentation, medical image registration, anatomical constraints, convolutional neural network, AC-RegNet Citation: Yang Y, Zheng J, Guo P, Gao Q, Guo ...

RECENT research has revealed that four commercially available natural language processing (NLP) tools used for annotating chest x-ray reports show high overall accuracy but exhibit significant ...

Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, ...

Several significant large-scale chest X-ray datasets have been pivotal in advancing medical imaging research. For instance, ChestX-ray8 and ChestX-ray14, released by the US National Institutes of ...

Thus began a 50-year decline in smoking. When the warning labels first appeared, around 42 percent of U.S. adults were daily cigarette smokers; by 2021, that portion had dropped to 11.5 percent.

Guendel, S., Ghesu, F.C., Grbic, S., Gibson, E., Georgescu, B., Maier, A. and Comaniciu, D. (2019) Multi-Task Learning for Chest X-Ray Abnormality Classification on Noisy Labels. has been cited by the ...