AIMC Topic: Thoracic Diseases
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Improved swin transformer-based thorax disease classification with optimal feature selection using chest X-ray.
PloS one
Jun 25, 2025
Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using...
Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification.
Artificial intelligence in medicine
Apr 23, 2025
U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our s...
Robust Stochastic Neural Ensemble Learning With Noisy Labels for Thoracic Disease Classification.
IEEE transactions on medical imaging
Jun 3, 2024
Chest radiography is the most common radiology examination for thoracic disease diagnosis, such as pneumonia. A tremendous number of chest X-rays prompt data-driven deep learning models in constructing computer-aided diagnosis systems for thoracic di...
Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.
Sensors (Basel, Switzerland)
Feb 24, 2024
Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that ...
CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification.
The international journal of cardiovascular imaging
Dec 27, 2023
The existing multilabel X-Ray image learning tasks generally contain much information on pathology co-occurrence and interdependency, which is very important for clinical diagnosis. However, the challenging part of this subject is to accurately diagn...
Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-Rays.
IEEE journal of biomedical and health informatics
Nov 10, 2022
Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsibl...
An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks.
Computational and mathematical methods in medicine
Sep 20, 2021
There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in ...
Contralaterally Enhanced Networks for Thoracic Disease Detection.
IEEE transactions on medical imaging
Aug 31, 2021
Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many simila...
Weighing features of lung and heart regions for thoracic disease classification.
BMC medical imaging
Jun 10, 2021
BACKGROUND: Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and he...
Contrast-Attentive Thoracic Disease Recognition With Dual-Weighting Graph Reasoning.
IEEE transactions on medical imaging
Apr 1, 2021
Automatic thoracic disease diagnosis is a rising research topic in the medical imaging community, with many potential applications. However, the inconsistent appearances and high complexities of various lesions in chest X-rays currently hinder the de...
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