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[HTML][HTML] Data augmentation: A comprehensive survey of modern approaches
A Mumuni, F Mumuni - Array, 2022 - Elsevier
To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
Diffusion models in medical imaging: A comprehensive survey
Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …
Medical image data augmentation: techniques, comparisons and interpretations
E Goceri - Artificial Intelligence Review, 2023 - Springer
Designing deep learning based methods with medical images has always been an attractive
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …
[HTML][HTML] A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities
Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …
developments in deep neural networks have contributed to significant advances in medical …
Deep learning approaches for data augmentation in medical imaging: a review
Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …
availability of training data remains a major challenge, particularly in the medical field where …
Generative models improve fairness of medical classifiers under distribution shifts
Abstract Domain generalization is a ubiquitous challenge for machine learning in
healthcare. Model performance in real-world conditions might be lower than expected …
healthcare. Model performance in real-world conditions might be lower than expected …
Diffusion models for medical image analysis: A comprehensive survey
Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …
Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization
COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It
usually is diagnosed by examining pathological photographs of the patient's lungs. There is …
usually is diagnosed by examining pathological photographs of the patient's lungs. There is …
Generative ai for medical imaging: extending the monai framework
Recent advances in generative AI have brought incredible breakthroughs in several areas,
including medical imaging. These generative models have tremendous potential not only to …
including medical imaging. These generative models have tremendous potential not only to …
Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet++
J Li, K Liu, Y Hu, H Zhang, AA Heidari, H Chen… - Computers in Biology …, 2023 - Elsevier
Computerized tomography (CT) is of great significance for the localization and diagnosis of
liver cancer. Many scholars have recently applied deep learning methods to segment CT …
liver cancer. Many scholars have recently applied deep learning methods to segment CT …