In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

In recent years, deep learning has been applied in the field of clinical medicine to process
large-scale medical images, for large-scale data screening, and in the diagnosis and …

Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

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 …

Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate
brain tumors by providing rich complementary information. Previous multi-modal MRI …

Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …

Multimodal learning which integrates data from diverse sensory modes plays a pivotal role
in artificial intelligence. However existing multimodal learning methods often struggle with …

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

Automatic segmentation of medical images plays an important role in the diagnosis of
diseases. On single-modal data, convolutional neural networks have demonstrated …