AIMC Topic: Thoracic Diseases
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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...
Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study.
Journal of medical Internet research
Feb 10, 2021
BACKGROUND: COVID-19 has spread very rapidly, and it is important to build a system that can detect it in order to help an overwhelmed health care system. Many research studies on chest diseases rely on the strengths of deep learning techniques. Alth...
The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting.
Clinical radiology
Nov 19, 2020
AIM: To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V).
Lesion-aware convolutional neural network for chest radiograph classification.
Clinical radiology
Oct 16, 2020
AIM: To investigate the performance of a deep-learning approach termed lesion-aware convolutional neural network (LACNN) to identify 14 different thoracic diseases on chest X-rays (CXRs).
Dense networks with relative location awareness for thorax disease identification.
Medical physics
Apr 14, 2019
PURPOSE: Chest X-ray is one of the most common examinations for diagnosing heart and lung diseases. Due to the existing of a large number of clinical cases, many automated diagnosis algorithms based on chest X-ray images have been proposed. To our kn...
Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs.
JAMA network open
Mar 1, 2019
IMPORTANCE: Interpretation of chest radiographs is a challenging task prone to errors, requiring expert readers. An automated system that can accurately classify chest radiographs may help streamline the clinical workflow.
Potential of a machine-learning model for dose optimization in CT quality assurance.
European radiology
Feb 19, 2019
OBJECTIVES: To evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study.
Automatic Localization and Identification of Thoracic Diseases from Chest X-rays with Deep Learning.
Current medical imaging
Jan 1, 2022
BACKGROUND: There are numerous difficulties in using deep learning to automatically locate and identify diseases in chest X-rays (CXR). The most prevailing two are the lack of labeled data of disease locations and poor model transferability between d...
Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen.
Radiologic clinics of North America
Nov 1, 2021
Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have ...
A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.
Current medical imaging
Jan 1, 2021
BACKGROUND: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at...
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