[HTML][HTML] Learning disentangled representations in the imaging domain
Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …
general representations even in the absence of, or with limited, supervision. A good general …
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 …
[HTML][HTML] Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
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 …
Learning calibrated medical image segmentation via multi-rater agreement modeling
In medical image analysis, it is typical to collect multiple annotations, each from a different
clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated …
clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated …
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 …
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 …
Knowledge distillation from multi-modal to mono-modal segmentation networks
The joint use of multiple imaging modalities for medical image segmentation has been
widely studied in recent years. The fusion of information from different modalities has …
widely studied in recent years. The fusion of information from different modalities has …
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 …