Self-supervised multi-modal hybrid fusion network for brain tumor segmentation
Accurate medical image segmentation of brain tumors is necessary for the diagnosing,
monitoring, and treating disease. In recent years, with the gradual emergence of multi …
monitoring, and treating disease. In recent years, with the gradual emergence of multi …
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 …
[Retracted] Automatic Segmentation of MRI of Brain Tumor Using Deep Convolutional Network
R Zhou, S Hu, B Ma, B Ma - BioMed Research International, 2022 - Wiley Online Library
Computer‐aided diagnosis and treatment of multimodal magnetic resonance imaging (MRI)
brain tumor image segmentation has always been a hot and significant topic in the field of …
brain tumor image segmentation has always been a hot and significant topic in the field of …
Overview of multi-modal brain tumor mr image segmentation
The precise segmentation of brain tumor images is a vital step towards accurate diagnosis
and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate …
and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate …
Multimodal transformer of incomplete MRI data for brain tumor segmentation
H Ting, M Liu - IEEE Journal of Biomedical and Health …, 2023 - ieeexplore.ieee.org
Accurate segmentation of brain tumors plays an important role for clinical diagnosis and
treatment. Multimodal magnetic resonance imaging (MRI) can provide rich and …
treatment. Multimodal magnetic resonance imaging (MRI) can provide rich and …
Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion
Accurate medical image segmentation commonly requires effective learning of the
complementary information from multimodal data. However, in clinical practice, we often …
complementary information from multimodal data. However, in clinical practice, we often …
Sf-net: A multi-task model for brain tumor segmentation in multimodal mri via image fusion
Automatic segmentation of brain tumor regions from multimodal MRI scans is of great clinical
significance. In this letter, we propose a “Segmentation-Fusion” multi-task model named SF …
significance. In this letter, we propose a “Segmentation-Fusion” multi-task model named SF …
Modality-pairing learning for brain tumor segmentation
Automatic brain tumor segmentation from multi-modality Magnetic Resonance Images (MRI)
using deep learning methods plays an important role in assisting the diagnosis and …
using deep learning methods plays an important role in assisting the diagnosis and …
Modality-level cross-connection and attentional feature fusion based deep neural network for multi-modal brain tumor segmentation
T Zhou - Biomedical Signal Processing and Control, 2023 - Elsevier
Brain tumor segmentation from Magnetic Resonance Imaging is essential for early diagnosis
and treatment planning for brain cancers in clinical practice. However, existing brain tumor …
and treatment planning for brain cancers in clinical practice. However, existing brain tumor …
A deep learning framework for segmenting brain tumors using MRI and synthetically generated CT images
KT Islam, S Wijewickrema, S O'leary - Sensors, 2022 - mdpi.com
Multi-modal three-dimensional (3-D) image segmentation is used in many medical
applications, such as disease diagnosis, treatment planning, and image-guided surgery …
applications, such as disease diagnosis, treatment planning, and image-guided surgery …