Transfer learning techniques for medical image analysis: A review
Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
Review on the evaluation and development of artificial intelligence for COVID-19 containment
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a
substantiated promise of continuous applicability in the real world domain. Artificial …
substantiated promise of continuous applicability in the real world domain. Artificial …
[HTML][HTML] PDAtt-Unet: Pyramid dual-decoder attention Unet for Covid-19 infection segmentation from CT-scans
Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been
widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose …
widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose …
Encodermi: Membership inference against pre-trained encoders in contrastive learning
Given a set of unlabeled images or (image, text) pairs, contrastive learning aims to pre-train
an image encoder that can be used as a feature extractor for many downstream tasks. In this …
an image encoder that can be used as a feature extractor for many downstream tasks. In this …
A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain
A Heidari, S Toumaj, NJ Navimipour, M Unal - Computers in Biology and …, 2022 - Elsevier
With the global spread of the COVID-19 epidemic, a reliable method is required for
identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits …
identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits …
[HTML][HTML] Medical image processing and COVID-19: a literature review and bibliometric analysis
COVID-19 crisis has placed medical systems over the world under unprecedented and
growing pressure. Medical imaging processing can help in the diagnosis, treatment, and …
growing pressure. Medical imaging processing can help in the diagnosis, treatment, and …
Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract
features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests …
features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests …
A systematic review on deep structured learning for COVID-19 screening using chest CT from 2020 to 2022
KC Santosh, D GhoshRoy, S Nakarmi - Healthcare, 2023 - mdpi.com
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel
coronavirus. The World Health Organization (WHO) designated it as a global pandemic on …
coronavirus. The World Health Organization (WHO) designated it as a global pandemic on …
Emb-trattunet: a novel edge loss function and transformer-CNN architecture for multi-classes pneumonia infection segmentation in low annotation regimes
One of the primary challenges in applying deep learning approaches to medical imaging is
the limited availability of data due to various factors. These factors include concerns about …
the limited availability of data due to various factors. These factors include concerns about …
Deep semi-supervised ultrasound image segmentation by using a shadow aware network with boundary refinement
F Chen, L Chen, W Kong, W Zhang… - … on Medical Imaging, 2023 - ieeexplore.ieee.org
Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of
diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time …
diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time …