[HTML][HTML] Deep learning for chest X-ray analysis: A survey
E Çallı, E Sogancioglu, B van Ginneken… - Medical Image …, 2021 - Elsevier
Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …
image analysis tasks. As the most commonly performed radiological exam, chest …
Clinical artificial intelligence applications in radiology: chest and abdomen
S Lee, RM Summers - Radiologic Clinics, 2021 - radiologic.theclinics.com
How wonderful would it be if an automated assistant would go through our daily chest
radiographs and sort out the ones that need our immediate attention; or maybe pick out the …
radiographs and sort out the ones that need our immediate attention; or maybe pick out the …
Automatic lung segmentation on chest X-rays using self-attention deep neural network
M Kim, BD Lee - Sensors, 2021 - mdpi.com
Accurate identification of the boundaries of organs or abnormal objects (eg, tumors) in
medical images is important in surgical planning and in the diagnosis and prognosis of …
medical images is important in surgical planning and in the diagnosis and prognosis of …
Learning deformable registration of medical images with anatomical constraints
Deformable image registration is a fundamental problem in the field of medical image
analysis. During the last years, we have witnessed the advent of deep learning-based image …
analysis. During the last years, we have witnessed the advent of deep learning-based image …
Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning
Objectives To take advantage of the deep learning algorithms to detect and calculate clot
burden of acute pulmonary embolism (APE) on computed tomographic pulmonary …
burden of acute pulmonary embolism (APE) on computed tomographic pulmonary …
Fast COVID-19 and pneumonia classification using chest X-ray images
As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus,
which has become the pandemic COVID-19. This aggressive disease deteriorates the …
which has become the pandemic COVID-19. This aggressive disease deteriorates the …
Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning
We examined the feasibility of explainable computer-aided detection of cardiomegaly in
routine clinical practice using segmentation-based methods. Overall, 793 retrospectively …
routine clinical practice using segmentation-based methods. Overall, 793 retrospectively …
Cardiomegaly detection on chest radiographs: Segmentation versus classification
In this study, we investigate the detection of cardiomegaly on frontal chest radiographs
through two alternative deep-learning approaches-via anatomical segmentation and via …
through two alternative deep-learning approaches-via anatomical segmentation and via …
H-SegNet: hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed …
Chest x-ray (CXR) is one of the most commonly used imaging techniques for the detection
and diagnosis of pulmonary diseases. One critical component in many computer-aided …
and diagnosis of pulmonary diseases. One critical component in many computer-aided …
Chest X-ray image analysis with combining 2D and 1D convolutional neural network based classifier for rapid cardiomegaly screening
JX Wu, CC Pai, CD Kan, PY Chen, WL Chen… - IEEE …, 2022 - ieeexplore.ieee.org
Cardiomegaly is an asymptomatic disease. Symptoms, such as palpitations, chest tightness,
and shortness of breath, may be the early indications of cardiac hypertrophy, which can be …
and shortness of breath, may be the early indications of cardiac hypertrophy, which can be …