Deep adversarial one-class learning for normal and abnormal chest radiograph classification
In machine learning, one-class classification tries to classify data of a specific category
amongst all data, by learning from a training set containing only the data of that unique …
amongst all data, by learning from a training set containing only the data of that unique …
Abnormal chest x-ray identification with generative adversarial one-class classifier
Being one of the most common diagnostic imaging tests, chest radiography requires timely
reporting of potential findings in the images. In this paper, we propose an end-to-end …
reporting of potential findings in the images. In this paper, we propose an end-to-end …
Learning invariant feature representation to improve generalization across chest x-ray datasets
Chest radiography is the most common medical image examination for screening and
diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level …
diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level …
End-to-end deep diagnosis of x-ray images
K Urinbayev, Y Orazbek, Y Nurambek… - 2020 42nd Annual …, 2020 - ieeexplore.ieee.org
We present an end-to-end deep learning frame-work for X-ray image diagnosis. As the first
step, our system determines whether a submitted image is an X-ray or not. After it classifies …
step, our system determines whether a submitted image is an X-ray or not. After it classifies …
Identifying disease-free chest x-ray images with deep transfer learning
Chest X-rays (CXRs) are among the most commonly used medical image modalities. They
are mostly used for screening, and an indication of disease typically results in subsequent …
are mostly used for screening, and an indication of disease typically results in subsequent …
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
B Bozorgtabar, D Mahapatra… - Computer vision and …, 2019 - Elsevier
Training robust deep learning (DL) systems for disease detection from medical images is
challenging due to limited images covering different disease types and severity. The …
challenging due to limited images covering different disease types and severity. The …
CheXphoto: 10,000+ photos and transformations of chest X-rays for benchmarking deep learning robustness
NA Phillips, P Rajpurkar, M Sabini… - … Learning for Health, 2020 - proceedings.mlr.press
Clinical deployment of deep learning algorithms for chest x-ray interpretation requires a
solution that can integrate into the vast spectrum of clinical workflows across the world. An …
solution that can integrate into the vast spectrum of clinical workflows across the world. An …
Bone suppression on chest radiographs with adversarial learning
Dual-energy (DE) chest radiography provides the capability of selectively imaging two
clinically relevant materials, namely soft tissues, and osseous structures, to better …
clinically relevant materials, namely soft tissues, and osseous structures, to better …
Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation
Deep learning algorithms require large amounts of labeled data which is difficult to attain for
medical imaging. Even if a particular dataset is accessible, a learned classifier struggles to …
medical imaging. Even if a particular dataset is accessible, a learned classifier struggles to …
Generative adversarial networks improve the reproducibility and discriminative power of radiomic features
S Marcadent, J Hofmeister, MG Preti… - Radiology: Artificial …, 2020 - pubs.rsna.org
Purpose To assess the contribution of a generative adversarial network (GAN) to improve
intermanufacturer reproducibility of radiomic features (RFs). Materials and Methods The …
intermanufacturer reproducibility of radiomic features (RFs). Materials and Methods The …