[HTML][HTML] A review: Deep learning for medical image segmentation using multi-modality fusion
Multi-modality is widely used in medical imaging, because it can provide multiinformation
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …
units are used to extract patterns from data in order to solve complex problems. Deep …
HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation
Recently, dense connections have attracted substantial attention in computer vision
because they facilitate gradient flow and implicit deep supervision during training …
because they facilitate gradient flow and implicit deep supervision during training …
A novel approach for brain tumour detection using deep learning based technique
Identifying the tumour's extent is a major challenge in planning treatment for brain tumours
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …
MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks
Purpose Automated synthetic computed tomography (sCT) generation based on magnetic
resonance imaging (MRI) images would allow for MRI‐only based treatment planning in …
resonance imaging (MRI) images would allow for MRI‐only based treatment planning in …
Attention gate resU-Net for automatic MRI brain tumor segmentation
Brain tumor segmentation technology plays a pivotal role in the process of diagnosis and
treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as …
treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as …
Hyperspectral imaging for clinical applications
J Yoon - BioChip Journal, 2022 - Springer
Measuring morphological and biochemical features of tissue is crucial for disease diagnosis
and surgical guidance, providing clinically significant information related to pathophysiology …
and surgical guidance, providing clinically significant information related to pathophysiology …
ME‐Net: multi‐encoder net framework for brain tumor segmentation
W Zhang, G Yang, H Huang, W Yang… - … Journal of Imaging …, 2021 - Wiley Online Library
MRI plays a vital role to evaluate brain tumor diagnosis and treatment planning. However,
the manual segmentation of the MRI image is strenuous. With the development of deep …
the manual segmentation of the MRI image is strenuous. With the development of deep …
Medical image segmentation with 3D convolutional neural networks: A survey
Computer-aided medical image analysis plays a significant role in assisting medical
practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present …
practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present …
Bu-net: Brain tumor segmentation using modified u-net architecture
The semantic segmentation of a brain tumor is of paramount importance for its treatment and
prevention. Recently, researches have proposed various neural network-based …
prevention. Recently, researches have proposed various neural network-based …