Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming
their negative repercussions is considered a hurdle in biomedical imaging. The combination …

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 …

Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging
(MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and …

Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting
various anatomical structures in medical images but often suffer from relatively poor …

Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-
region analysis of brain tumors. Plenty of methods have been proposed for automatic brain …

Automated brain tumor segmentation is crucial for aiding brain disease diagnosis and
evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely …

Multi-modality medical data provide complementary information, and hence have been
widely explored for computer-aided AD diagnosis. However, the research is hindered by the …

Transfer learning strategies were used to develop a unique method in the field of medicine.
Investigation in this study suggests an ensemble technique for early brain tumor detection …

The fusion of multi-modal data, eg, pathology slides and genomic profiles, can provide
complementary information and benefit glioma grading. However, genomic profiles are …

Multimodal learning has shown great potentials in numerous scenes and attracts increasing
interest recently. However, it often encounters the problem of missing modality data and thus …