[HTML][HTML] Deep learning for brain tumor segmentation: a survey of state-of-the-art
Quantitative analysis of the brain tumors provides valuable information for understanding the
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
[HTML][HTML] Deep learning based synthesis of MRI, CT and PET: Review and analysis
Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …
aiming to overcome the challenges associated with acquiring multiple image modalities for …
[HTML][HTML] Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images
Among researchers using traditional and new machine learning and deep learning
techniques, 2D medical image segmentation models are popular. Additionally, 3D …
techniques, 2D medical image segmentation models are popular. Additionally, 3D …
[HTML][HTML] A deep multi-task learning framework for brain tumor segmentation
H Huang, G Yang, W Zhang, X Xu, W Yang… - Frontiers in …, 2021 - frontiersin.org
Glioma is the most common primary central nervous system tumor, accounting for about half
of all intracranial primary tumors. As a non-invasive examination method, MRI has an …
of all intracranial primary tumors. As a non-invasive examination method, MRI has an …
An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2 …
OBJECTIVE Automatic segmentation of vestibular schwannomas (VSs) from MRI could
significantly improve clinical workflow and assist in patient management. Accurate tumor …
significantly improve clinical workflow and assist in patient management. Accurate tumor …
[HTML][HTML] SD-UNET: Stripping down U-net for segmentation of biomedical images on platforms with low computational budgets
PK Gadosey, Y Li, EA Agyekum, T Zhang, Z Liu… - Diagnostics, 2020 - mdpi.com
During image segmentation tasks in computer vision, achieving high accuracy performance
while requiring fewer computations and faster inference is a big challenge. This is especially …
while requiring fewer computations and faster inference is a big challenge. This is especially …
[HTML][HTML] The brain tumor segmentation (brats) challenge 2023: glioma segmentation in sub-saharan Africa patient population (brats-africa)
Gliomas are the most common type of primary brain tumors. Although gliomas are relatively
rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years …
rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years …
[HTML][HTML] Kidney tumor semantic segmentation using deep learning: A survey of state-of-the-art
A Abdelrahman, S Viriri - Journal of imaging, 2022 - mdpi.com
Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic
procedures for early detection and diagnosis are crucial. Some difficulties with manual …
procedures for early detection and diagnosis are crucial. Some difficulties with manual …
Unsupervised brain tumor segmentation using a symmetric-driven adversarial network
The aim of this study was to computationally model, in an unsupervised manner, a manifold
of symmetry variations in normal brains, such that the learned manifold can be used to …
of symmetry variations in normal brains, such that the learned manifold can be used to …
[HTML][HTML] Encrypted federated learning for secure decentralized collaboration in cancer image analysis
Artificial intelligence (AI) has a multitude of applications in cancer research and oncology.
However, the training of AI systems is impeded by the limited availability of large datasets …
However, the training of AI systems is impeded by the limited availability of large datasets …