Transfer learning for medical image classification: a literature review
HE Kim, A Cosa-Linan, N Santhanam, M Jannesari… - BMC medical …, 2022 - Springer
Background Transfer learning (TL) with convolutional neural networks aims to improve
performances on a new task by leveraging the knowledge of similar tasks learned in …
performances on a new task by leveraging the knowledge of similar tasks learned in …
A sco** review of transfer learning research on medical image analysis using ImageNet
Objective Employing transfer learning (TL) with convolutional neural networks (CNNs), well-
trained on non-medical ImageNet dataset, has shown promising results for medical image …
trained on non-medical ImageNet dataset, has shown promising results for medical image …
Deep learning in medical imaging and radiation therapy
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse …
Several infectious diseases have affected the lives of many people and have caused great
dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly …
dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly …
Machine learning on neutron and x-ray scattering and spectroscopies
Neutron and x-ray scattering represent two classes of state-of-the-art materials
characterization techniques that measure materials structural and dynamical properties with …
characterization techniques that measure materials structural and dynamical properties with …
Current applications and future directions of deep learning in musculoskeletal radiology
P Chea, JC Mandell - Skeletal radiology, 2020 - Springer
Deep learning with convolutional neural networks (CNN) is a rapidly advancing subset of
artificial intelligence that is ideally suited to solving image-based problems. There are an …
artificial intelligence that is ideally suited to solving image-based problems. There are an …
Deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers' health examination data
We aimed to use deep learning to detect tuberculosis in chest radiographs in annual
workers' health examination data and compare the performances of convolutional neural …
workers' health examination data and compare the performances of convolutional neural …
Deep learning classifiers for automated detection of gonioscopic angle closure based on anterior segment OCT images
Purpose To develop and test deep learning classifiers that detect gonioscopic angle closure
and primary angle closure disease (PACD) based on fully automated analysis of anterior …
and primary angle closure disease (PACD) based on fully automated analysis of anterior …
[HTML][HTML] Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature
The major and upward trend in the number of published research related to rheumatic and
musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the …
musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the …
Democratized image analytics by visual programming through integration of deep models and small-scale machine learning
Abstract Analysis of biomedical images requires computational expertize that are
uncommon among biomedical scientists. Deep learning approaches for image analysis …
uncommon among biomedical scientists. Deep learning approaches for image analysis …