Neuroimaging and deep learning for brain stroke detection-A review of recent advancements and future prospects
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 …
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 …
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
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 …
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …
Brain tumor segmentation and survival prediction using 3D attention UNet
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 …
tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using …
Intracranial hemorrhage detection using parallel deep convolutional models and boosting mechanism
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 …
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
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 …
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 …
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
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans
is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores …
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
This study evaluated deep learning algorithms for semantic segmentation and quantification
of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular …
of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular …
[HTML][HTML] Automated hematoma segmentation and outcome prediction for patients with traumatic brain injury
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 …
brain hematoma segmentation and outcome prediction for patients with TBI can effectively …