[HTML][HTML] Transfer learning in magnetic resonance brain imaging: A systematic review
(1) Background: Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …
Medical image segmentation on mri images with missing modalities: A review
R Azad, N Khosravi, M Dehghanmanshadi… - arXiv preprint arXiv …, 2022 - arxiv.org
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming
their negative repercussions is considered a hurdle in biomedical imaging. The combination …
their negative repercussions is considered a hurdle in biomedical imaging. The combination …
mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation
Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to
joint learning of multimodal images. However, in clinical practice, it is not always possible to …
joint learning of multimodal images. However, in clinical practice, it is not always possible to …
Latent correlation representation learning for brain tumor segmentation with missing MRI modalities
Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain
tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics …
tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics …
Hi-net: hybrid-fusion network for multi-modal MR image synthesis
Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …
RFNet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation
Most existing brain tumor segmentation methods usually exploit multi-modal magnetic
resonance imaging (MRI) images to achieve high segmentation performance. However, the …
resonance imaging (MRI) images to achieve high segmentation performance. However, the …
Multi-modal learning with missing modality via shared-specific feature modelling
The missing modality issue is critical but non-trivial to be solved by multi-modal models.
Current methods aiming to handle the missing modality problem in multi-modal tasks, either …
Current methods aiming to handle the missing modality problem in multi-modal tasks, either …
D2-Net: Dual Disentanglement Network for Brain Tumor Segmentation With Missing Modalities
Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …
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 …
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 …