[HTML][HTML] Medical image data augmentation: techniques, comparisons and interpretations
E Goceri - Artificial Intelligence Review, 2023 - Springer
Designing deep learning based methods with medical images has always been an attractive
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …
[HTML][HTML] 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 …
A review on convolutional neural networks for brain tumor segmentation: methods, datasets, libraries, and future directions
MK Balwant - Irbm, 2022 - Elsevier
Objectives Accurate and reliable segmentation of brain tumors from MRI images helps in
planning an enhanced treatment and increases the life expectancy of patients. However, the …
planning an enhanced treatment and increases the life expectancy of patients. However, the …
Machine learning and deep learning for brain tumor MRI image segmentation
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …
Brain tumor segmentation in MRI images using nonparametric localization and enhancement methods with U-net
Purpose: Segmentation is one of the critical steps in analyzing medical images since it
provides meaningful information for the diagnosis, monitoring, and treatment of brain tumors …
provides meaningful information for the diagnosis, monitoring, and treatment of brain tumors …
Cross-dimensional transfer learning in medical image segmentation with deep learning
Over the last decade, convolutional neural networks have emerged and advanced the state-
of-the-art in various image analysis and computer vision applications. The performance of …
of-the-art in various image analysis and computer vision applications. The performance of …
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 …
DenseTrans: multimodal brain tumor segmentation using swin transformer
L ZongRen, W Silamu, W Yuzhen, W Zhe - IEEE Access, 2023 - ieeexplore.ieee.org
Aiming at the task of automatic brain tumor segmentation, this paper proposes a new
DenseTrans network. In order to alleviate the problem that convolutional neural networks …
DenseTrans network. In order to alleviate the problem that convolutional neural networks …
Brain tumour segmentation with a muti-pathway ResNet based UNet
A Saha, YD Zhang, SC Satapathy - Journal of Grid Computing, 2021 - Springer
Automatic segmentation of brain tumour regions is essential in today's scenario for proper
diagnosis and treatment of the disease. Gliomas can appear in any region and can be of any …
diagnosis and treatment of the disease. Gliomas can appear in any region and can be of any …
[HTML][HTML] HGG and LGG brain tumor segmentation in multi-modal MRI using pretrained convolutional neural networks of Amazon Sagemaker
Automatic brain tumor segmentation from multimodal MRI plays a significant role in assisting
the diagnosis, treatment, and surgery of glioblastoma and lower glade glioma. In this article …
the diagnosis, treatment, and surgery of glioblastoma and lower glade glioma. In this article …