A review on the use of deep learning for medical images segmentation
M Aljabri, M AlGhamdi - Neurocomputing, 2022 - Elsevier
Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …
images. They have been used extensively for medical image segmentation as the first and …
Artificial intelligence and COVID-19 using chest CT scan and chest X-ray images: machine learning and deep learning approaches for diagnosis and treatment
Objective: To report an overview and update on Artificial Intelligence (AI) and COVID-19
using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine …
using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine …
Unsupervised domain adaptation via deep conditional adaptation network
Unsupervised domain adaptation (UDA) aims to generalize the supervised model trained on
a source domain to an unlabeled target domain. Previous works mainly rely on the marginal …
a source domain to an unlabeled target domain. Previous works mainly rely on the marginal …
Source free semi-supervised transfer learning for diagnosis of mental disorders on fmri scans
The high prevalence of mental disorders gradually poses a huge pressure on the public
healthcare services. Deep learning-based computer-aided diagnosis (CAD) has emerged to …
healthcare services. Deep learning-based computer-aided diagnosis (CAD) has emerged to …
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …
imaging. However, these approaches primarily focus on supervised learning, assuming that …
[HTML][HTML] Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning
Abstract Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting
millions of people's health and lives in jeopardy. Detecting infected patients early on chest …
millions of people's health and lives in jeopardy. Detecting infected patients early on chest …
Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data
Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance
(MR) images assists in the guidance of treatment intensity, thus reducing the risk of …
(MR) images assists in the guidance of treatment intensity, thus reducing the risk of …
Attentive continuous generative self-training for unsupervised domain adaptive medical image translation
Self-training is an important class of unsupervised domain adaptation (UDA) approaches
that are used to mitigate the problem of domain shift, when applying knowledge learned …
that are used to mitigate the problem of domain shift, when applying knowledge learned …
Continual nuclei segmentation via prototype-wise relation distillation and contrastive learning
Deep learning models have achieved remarkable success in multi-type nuclei
segmentation. These models are mostly trained at once with the full annotation of all types of …
segmentation. These models are mostly trained at once with the full annotation of all types of …
A deep learning model for the diagnosis and discrimination of gram-positive and gram-negative bacterial pneumonia for children using chest radiography images and …
R Wen, P Xu, Y Cai, F Wang, M Li… - Infection and Drug …, 2023 - Taylor & Francis
Purpose This study aimed to develop a deep learning model based on chest radiography
(CXR) images and clinical data to accurately classify gram-positive and gram-negative …
(CXR) images and clinical data to accurately classify gram-positive and gram-negative …