How far have we come? Artificial intelligence for chest radiograph interpretation
Due to recent advances in artificial intelligence, there is renewed interest in automating
interpretation of imaging tests. Chest radiographs are particularly interesting due to many …
interpretation of imaging tests. Chest radiographs are particularly interesting due to many …
SuperPCA: A superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery
As an unsupervised dimensionality reduction method, the principal component analysis
(PCA) has been widely considered as an efficient and effective preprocessing step for …
(PCA) has been widely considered as an efficient and effective preprocessing step for …
M3FuNet:An Unsupervised Multivariate Feature Fusion Network for Hyperspectral Image Classification
H Chen, H Long, T Chen, Y Song… - … on Geoscience and …, 2024 - ieeexplore.ieee.org
Hyperspectral image (HSI) spectral-spatial joint feature (FE) extraction methods generally
suffer from low feature retention and weak spatial–spectral dependence, which will lead to …
suffer from low feature retention and weak spatial–spectral dependence, which will lead to …
Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme
Abstract Machine learning techniques have been widely used for abnormality detection in
medical images. Chest X-ray images (CXR) are among the non-invasive diagnostic tools …
medical images. Chest X-ray images (CXR) are among the non-invasive diagnostic tools …
Image-to-images translation for multi-task organ segmentation and bone suppression in chest x-ray radiography
Chest X-ray radiography is one of the earliest medical imaging technologies and remains
one of the most widely-used for diagnosis, screening, and treatment follow up of diseases …
one of the most widely-used for diagnosis, screening, and treatment follow up of diseases …
[HTML][HTML] Efficient patch-wise semantic segmentation for large-scale remote sensing images
Y Liu, Q Ren, J Geng, M Ding, J Li - Sensors, 2018 - mdpi.com
Efficient and accurate semantic segmentation is the key technique for automatic remote
sensing image analysis. While there have been many segmentation methods based on …
sensing image analysis. While there have been many segmentation methods based on …
Spatial feature and resolution maximization GAN for bone suppression in chest radiographs
Abstract Background and Objective: Chest radiographs (CXR) are in great demand for
visualizing the pathology of the lungs. However, the appearance of bones in the lung region …
visualizing the pathology of the lungs. However, the appearance of bones in the lung region …
[HTML][HTML] Detecting tuberculosis-consistent findings in lateral chest X-rays using an ensemble of CNNs and vision transformers
Research on detecting Tuberculosis (TB) findings on chest radiographs (or Chest X-rays:
CXR) using convolutional neural networks (CNNs) has demonstrated superior performance …
CXR) using convolutional neural networks (CNNs) has demonstrated superior performance …
Comparing deep learning models for population screening using chest radiography
R Sivaramakrishnan, S Antani… - Medical Imaging …, 2018 - spiedigitallibrary.org
According to the World Health Organization (WHO), tuberculosis (TB) remains the most
deadly infectious disease in the world. In a 2015 global annual TB report, 1.5 million TB …
deadly infectious disease in the world. In a 2015 global annual TB report, 1.5 million TB …
[HTML][HTML] Urban area detection in very high resolution remote sensing images using deep convolutional neural networks
Detecting urban areas from very high resolution (VHR) remote sensing images plays an
important role in the field of Earth observation. The recently-developed deep convolutional …
important role in the field of Earth observation. The recently-developed deep convolutional …