Neuroimaging and deep learning for brain stroke detection-A review of recent advancements and future prospects

R Karthik, R Menaka, A Johnson, S Anand - Computer Methods and …, 2020 - Elsevier
Background and objective In recent years, deep learning algorithms have created a massive
impact on addressing research challenges in different domains. The medical field also …

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

DT Wagner, L Tilmans, K Peng, M Niedermeier, M Rohl… - Diagnostics, 2023 - mdpi.com
There is an expanding body of literature that describes the application of deep learning and
other machine learning and artificial intelligence methods with potential relevance to …

Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks

M Burduja, RT Ionescu, N Verga - Sensors, 2020 - mdpi.com
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …

Brain tumor segmentation and survival prediction using 3D attention UNet

M Islam, VS Vibashan, VJM Jose, N Wijethilake… - … Sclerosis, Stroke and …, 2020 - Springer
In this work, we develop an attention convolutional neural network (CNN) to segment brain
tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using …

Intracranial hemorrhage detection using parallel deep convolutional models and boosting mechanism

M Asif, MA Shah, HA Khattak, S Mussadiq, E Ahmed… - Diagnostics, 2023 - mdpi.com
Intracranial hemorrhage (ICH) can lead to death or disability, which requires immediate
action from radiologists. Due to the heavy workload, less experienced staff, and the …

3D deep neural network segmentation of intracerebral hemorrhage: development and validation for clinical trials

MF Sharrock, WA Mould, H Ali, M Hildreth, IA Awad… - Neuroinformatics, 2021 - Springer
Intracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. This leads
to significant morbidity and mortality, the likelihood of which is predicated on the size of the …

Deep transfer learning for automatic prediction of hemorrhagic stroke on CT images

BN Rao, S Mohanty, K Sen, UR Acharya… - … Methods in Medicine, 2022 - Wiley Online Library
Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke which
occurs due to ruptures of weakened blood vessel in brain tissue. It is a serious medical …

The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge

X Li, G Luo, K Wang, H Wang, J Liu, X Liang… - arXiv preprint arXiv …, 2023 - arxiv.org
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans
is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores …

Semantic segmentation of spontaneous intracerebral hemorrhage, intraventricular hemorrhage, and associated edema on CT images using deep learning

YE Kok, S Pszczolkowski, ZK Law, A Ali… - Radiology: Artificial …, 2022 - pubs.rsna.org
This study evaluated deep learning algorithms for semantic segmentation and quantification
of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular …

[HTML][HTML] Automated hematoma segmentation and outcome prediction for patients with traumatic brain injury

H Yao, C Williamson, J Gryak, K Najarian - Artificial Intelligence in Medicine, 2020 - Elsevier
Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Automated
brain hematoma segmentation and outcome prediction for patients with TBI can effectively …