Self-supervised semantic segmentation: Consistency over transformation
S Karimijafarbigloo, R Azad… - Proceedings of the …, 2023 - openaccess.thecvf.com
Accurate medical image segmentation is of utmost importance for enabling automated
clinical decision procedures. However, prevailing supervised deep learning approaches for …
clinical decision procedures. However, prevailing supervised deep learning approaches for …
Beyond accuracy and precision: a robust deep learning framework to enhance the resilience of face mask detection models against adversarial attacks
BUH Sheikh, A Zafar - Evolving Systems, 2024 - Springer
As the world continues to grapple with the COVID-19 pandemic, the face mask detection
system has become an essential tool in containing the spread of the virus. However, our …
system has become an essential tool in containing the spread of the virus. However, our …
Domain perceptive-pruning and fine-tuning the pre-trained model for heterogeneous transfer learning in cross domain prediction
During the process of real-time monitoring, low sampling rate make it difficult to construct a
prediction model of variable due to the lack of data. Transfer learning addresses the …
prediction model of variable due to the lack of data. Transfer learning addresses the …
A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays
M Abdullah, F berhe Abrha, B Kedir, TT Tagesse - Heliyon, 2024 - cell.com
Abstract Coronavirus disease (COVID-2019) is emerging in Wuhan, China in 2019. It has
spread throughout the world since the year 2020. Millions of people were affected and …
spread throughout the world since the year 2020. Millions of people were affected and …
Conditional cascaded network (CCN) approach for diagnosis of COVID-19 in chest X-ray and CT images using transfer learning
AEE Rashed, WM Bahgat - Biomedical Signal Processing and Control, 2024 - Elsevier
The COVID-19 pandemic has caused substantial global health and economic damage, with
over five million confirmed cases worldwide. The importance of the rapid, accurate diagnosis …
over five million confirmed cases worldwide. The importance of the rapid, accurate diagnosis …
An adversarial multi-source transfer learning method for the stability analysis of methane hydrate-bearing sediments
This study presents an innovative adversarial multi-source transfer learning approach to
enhance submarine hydrate slope stability predictions in the face of small and varied …
enhance submarine hydrate slope stability predictions in the face of small and varied …
Data science in healthcare monitoring under covid-19 detection by extended hybrid leader-based compressed neural network
Abstract The Severe Acute Respiratory Syndrome CoronoVirus2 (SARS-CoV-2) causes the
infectious illness Covid-19 (Corona Virus Disease of 2019). The majority of virus-infected …
infectious illness Covid-19 (Corona Virus Disease of 2019). The majority of virus-infected …
New attention-gated residual deep convolutional network for accurate lung segmentation in chest x-rays
Chest x-rays (CXRs) are broadly used in clinical practice to diagnose pulmonary diseases.
Developing reliable computer-aided diagnosis (CAD) systems to automate the interpretation …
Developing reliable computer-aided diagnosis (CAD) systems to automate the interpretation …
Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification
L Jiang, J Xie, Z Bi - Applied Sciences, 2024 - mdpi.com
The rupture of unstable plaques is a major cause of acute cardiovascular events. The early
assessment of carotid plaques can significantly reduce the cardiovascular risks, so …
assessment of carotid plaques can significantly reduce the cardiovascular risks, so …
Reducing Uncertainty in 3D Medical Image Segmentation under Limited Annotations through Contrastive Learning
S Jarimijafarbigloo, R Azad, A Kazerouni… - Medical Imaging with … - openreview.net
Despite recent successes in semi-supervised learning for natural image segmentation,
applying these methods to medical images presents challenges in obtaining discriminative …
applying these methods to medical images presents challenges in obtaining discriminative …