Brain tumors have become a severe medical complication in recent years due to their high
fatality rate. Radiologists segment the tumor manually, which is time-consuming, error …

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

The exponential growth of deep learning networks has allowed us to tackle complex tasks,
even in fields as complicated as medicine. However, using these models requires a large …

Simple Summary The ongoing advancements in deep learning, notably its use in predicting
cancer survival through genomic data analysis, calls for an up-to-date review. This paper …

In medical vision, different imaging modalities provide complementary information. However,
in practice, not all modalities may be available during inference or even training. Previous …

Multimodal MR brain tumor segmentation is one of the hottest issues in the community of
medical image processing. However, acquiring the complete set of MR modalities is not …

The most common and aggressive malignant brain tumor in adults is glioma, which leads to
short life expectancy. A reliable and efficient automatic segmentation method is beneficial for …

Technology-assisted diagnosis is increasingly important in healthcare systems. Brain tumors
are a leading cause of death worldwide, and treatment plans rely heavily on accurate …

Accurate brain tumor segmentation is an essential step for clinical diagnosis and surgical
treatment. Multimodal brain tumor segmentation strongly relies on an effective fusion method …

Survival prediction of patients affected by brain tumors provides essential information to
guide surgical planning, adjuvant treatment selection, and patient counseling. Current …