Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: a survey
Management of brain tumors is based on clinical and radiological information with
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: A prospective survey
Radiologists encounter significant challenges when segmenting and determining brain
tumors in patients because this information assists in treatment planning. The utilization of …
tumors in patients because this information assists in treatment planning. The utilization of …
Deep learning based brain tumor segmentation: a survey
Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
Flexible fusion network for multi-modal brain tumor segmentation
Automated brain tumor segmentation is crucial for aiding brain disease diagnosis and
evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely …
evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely …
Enhancing brain tumor segmentation accuracy through scalable federated learning with advanced data privacy and security measures
Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment
while preserving patient data privacy and security. Traditional centralized approaches often …
while preserving patient data privacy and security. Traditional centralized approaches often …
DAUnet: A U-shaped network combining deep supervision and attention for brain tumor segmentation
Y Feng, Y Cao, D An, P Liu, X Liao, B Yu - Knowledge-Based Systems, 2024 - Elsevier
In MRI images, the brain tumor area varies greatly between individuals, and only relying on
the judgment of clinicians is prone to misdiagnosis and misjudgment. Consequently, utilizing …
the judgment of clinicians is prone to misdiagnosis and misjudgment. Consequently, utilizing …
Twist-Net: A multi-modality transfer learning network with the hybrid bilateral encoder for hypopharyngeal cancer segmentation
S Zhang, Y Miao, J Chen, X Zhang, L Han… - Computers in Biology …, 2023 - Elsevier
Hypopharyngeal cancer (HPC) is a rare disease. Therefore, it is a challenge to automatically
segment HPC tumors and metastatic lymph nodes (HPC risk areas) from medical images …
segment HPC tumors and metastatic lymph nodes (HPC risk areas) from medical images …
MimicNet: mimicking manual delineation of human expert for brain tumor segmentation from multimodal MRIs
Z Liu, Y Cheng, T Tan, T Shinichi - Applied Soft Computing, 2023 - Elsevier
Existing deep neural networks for brain tumor segmentation from multimodal MRIs rely
predominantly on standard segmentation architectures, overlooking the underlying rules in …
predominantly on standard segmentation architectures, overlooking the underlying rules in …
Self-supervised tumor segmentation with sim2real adaptation
This paper targets on self-supervised tumor segmentation. We make the following
contributions:(i) we take inspiration from the observation that tumors are often characterised …
contributions:(i) we take inspiration from the observation that tumors are often characterised …
MM-UNet: A multimodality brain tumor segmentation network in MRI images
The global annual incidence of brain tumors is approximately seven out of 100,000,
accounting for 2% of all tumors. The mortality rate ranks first among children under 12 and …
accounting for 2% of all tumors. The mortality rate ranks first among children under 12 and …