Deep learning approaches for data augmentation in medical imaging: a review
A Kebaili, J Lapuyade-Lahorgue, S Ruan - Journal of Imaging, 2023 - mdpi.com
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 …
[HTML][HTML] Review on deep learning fetal brain segmentation from Magnetic Resonance images
T Ciceri, L Squarcina, A Giubergia, A Bertoldo… - Artificial intelligence in …, 2023 - Elsevier
Brain segmentation is often the first and most critical step in quantitative analysis of the brain
for many clinical applications, including fetal imaging. Different aspects challenge the …
for many clinical applications, including fetal imaging. Different aspects challenge the …
An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset
It is critical to quantitatively analyse the developing human fetal brain in order to fully
understand neurodevelopment in both normal fetuses and those with congenital disorders …
understand neurodevelopment in both normal fetuses and those with congenital disorders …
[HTML][HTML] On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images
Deep learning-based segmentation methods provide an effective and automated way for
assessing the structure and function of the heart in cardiac magnetic resonance (CMR) …
assessing the structure and function of the heart in cardiac magnetic resonance (CMR) …
Can segmentation models be trained with fully synthetically generated data?
In order to achieve good performance and generalisability, medical image segmentation
models should be trained on sizeable datasets with sufficient variability. Due to ethics and …
models should be trained on sizeable datasets with sufficient variability. Due to ethics and …
[HTML][HTML] Preserving data privacy in machine learning systems
The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …
the need to collect and process large volumes of data, some of which are considered …
Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks
This paper explores the use of 'conditional convolutional generative adversarial
networks'(CDCGAN) for image-based leak detection and localization (LD&L) in water …
networks'(CDCGAN) for image-based leak detection and localization (LD&L) in water …
[HTML][HTML] Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
Despite technological and medical advances, the detection, interpretation, and treatment of
cancer based on imaging data continue to pose significant challenges. These include inter …
cancer based on imaging data continue to pose significant challenges. These include inter …
Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening
R Chen, W Zhang, F Song, H Yu, D Cao, Y Zheng… - NPJ digital …, 2024 - nature.com
Age-related macular degeneration (AMD) is the leading cause of central vision impairment
among the elderly. Effective and accurate AMD screening tools are urgently needed …
among the elderly. Effective and accurate AMD screening tools are urgently needed …
Conditional diffusion models for semantic 3d medical image synthesis
The demand for artificial intelligence (AI) in healthcare is rapidly increasing. However,
significant challenges arise from data scarcity and privacy concerns, particularly in medical …
significant challenges arise from data scarcity and privacy concerns, particularly in medical …