AIMC Topic: Lung Diseases

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Research on Chest Disease Recognition Based on Deep Hierarchical Learning Algorithm.

Journal of healthcare engineering Jan 7, 2022
Chest X-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases. However, it is still a challenge for exper...
Algorithms Deep Learning Lung Diseases Humans Neural Networks, Computer
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Wet-dry-wet drug screen leads to the synthesis of TS1, a novel compound reversing lung fibrosis through inhibition of myofibroblast differentiation.

Cell death & disease Dec 17, 2021
Therapies halting the progression of fibrosis are ineffective and limited. Activated myofibroblasts are emerging as important targets in the progression of fibrotic diseases. Previously, we performed a high-throughput screen on lung fibroblasts and s...
Drug Discovery Mice Lung Diseases Bleomycin Myofibroblasts Transfection Idiopathic Pulmonary Fibrosis High-Throughput Screening Assays Machine Learning Drug Screening Assays, Antitumor Humans Animals Cell Differentiation
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MARnet: multi-scale adaptive residual neural network for chest X-ray images recognition of lung diseases.

Mathematical biosciences and engineering : MBE Nov 15, 2021
Chest X-ray image is an important clinical diagnostic reference to lung diseases that is a serious threat to human health. At present, with the rapid development of computer vision and deep learning technology, many scholars have carried out the frui...
Lung Deep Learning Lung Diseases Humans Neural Networks, Computer X-Rays
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The smell of lung disease: a review of the current status of electronic nose technology.

Respiratory research Sep 17, 2021
There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nos...
Exhalation Electronic Nose Lung Diseases Smell Machine Learning Precision Medicine Humans
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Optimal number of strong labels for curriculum learning with convolutional neural network to classify pulmonary abnormalities in chest radiographs.

Computers in biology and medicine Aug 9, 2021
BACKGROUND AND OBJECTIVE: It is important to alleviate annotation efforts and costs by efficiently training on medical images. We performed a stress test on several strong labels for curriculum learning with a convolutional neural network to differen...
Neural Networks, Computer Humans Learning Lung Diseases Radiography Curriculum
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CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection.

IEEE journal of biomedical and health informatics Aug 5, 2021
This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from respiratory sound recordings. The framework begins with front-end feature ext...
Auscultation Respiratory Sounds Lung Lung Diseases Humans Neural Networks, Computer
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A novel strategy to develop deep learning for image super-resolution using original ultra-high-resolution computed tomography images of lung as training dataset.

Japanese journal of radiology Jul 28, 2021
PURPOSE: To improve the image quality of inflated fixed cadaveric human lungs by utilizing ultra-high-resolution computed tomography (U-HRCT) as a training dataset for super-resolution processing using deep learning (SR-DL).
Image Processing, Computer-Assisted Lung Diseases Lung Deep Learning Humans Tomography, X-Ray Computed
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Three-stage segmentation of lung region from CT images using deep neural networks.

BMC medical imaging Jul 15, 2021
BACKGROUND: Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy i...
Neural Networks, Computer Algorithms Humans Deep Learning Tomography, X-Ray Computed Lung Diseases Lung
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Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.

European journal of nuclear medicine and molecular imaging Jun 16, 2021
PURPOSE: To develop and evaluate the effectiveness of a deep learning framework (3D-ResNet) based on CT images to distinguish nontuberculous mycobacterium lung disease (NTM-LD) from Mycobacterium tuberculosis lung disease (MTB-LD).
Diagnosis, Differential Tomography, X-Ray Computed Humans Mycobacterium tuberculosis Tuberculosis Lung Diseases Retrospective Studies Nontuberculous Mycobacteria Deep Learning
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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...
Deep Learning ROC Curve Humans Lung Heart Diseases Heart Radiography, Thoracic Lung Diseases Thoracic Diseases
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