A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions
The recent developments of deep learning support the identification and classification of
lung diseases in medical images. Hence, numerous work on the detection of lung disease …
lung diseases in medical images. Hence, numerous work on the detection of lung disease …
Cavitary tuberculosis: the gateway of disease transmission
ME Urbanowski, AA Ordonez… - The Lancet Infectious …, 2020 - thelancet.com
Tuberculosis continues to be a major threat to global health. Cavitation is a dangerous
consequence of pulmonary tuberculosis associated with poor outcomes, treatment relapse …
consequence of pulmonary tuberculosis associated with poor outcomes, treatment relapse …
Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization
Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one
of the top 10 leading causes of death. Accurate and early detection of TB is very important …
of the top 10 leading causes of death. Accurate and early detection of TB is very important …
Contrastive self-supervised learning from 100 million medical images with optional supervision
Purpose Building accurate and robust artificial intelligence systems for medical image
assessment requires the creation of large sets of annotated training examples. However …
assessment requires the creation of large sets of annotated training examples. However …
Deep learning detection of active pulmonary tuberculosis at chest radiography matched the clinical performance of radiologists
S Kazemzadeh, J Yu, S Jamshy, R Pilgrim, Z Nabulsi… - Radiology, 2023 - pubs.rsna.org
Background The World Health Organization (WHO) recommends chest radiography to
facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise …
facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise …
Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization
Features in images' backgrounds can spuriously correlate with the images' classes,
representing background bias. They can influence the classifier's decisions, causing …
representing background bias. They can influence the classifier's decisions, causing …
Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases
In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms
that resist conventional antibiotic treatment has steadily increased. Thus, it is now …
that resist conventional antibiotic treatment has steadily increased. Thus, it is now …
AI‐Assisted Tuberculosis Detection and Classification from Chest X‐Rays Using a Deep Learning Normalization‐Free Network Model
Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is
imperative to detect cases of TB as early as possible because if left untreated, there is a 70 …
imperative to detect cases of TB as early as possible because if left untreated, there is a 70 …
Tuberculosis chest X-ray detection using CNN-based hybrid segmentation and classification approach
Tuberculosis still significantly impacts the world's population, with more than 10 million
people getting sick each year. Researchers have focused on developing computer-aided …
people getting sick each year. Researchers have focused on developing computer-aided …
LINCS Data Portal 2.0: next generation access point for perturbation-response signatures
Abstract The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH
Common Fund program with the goal of generating a large-scale and comprehensive …
Common Fund program with the goal of generating a large-scale and comprehensive …