CMAF-Net: a cross-modal attention fusion-based deep neural network for incomplete multi-modal brain tumor segmentation.

ABSTRACT:

Background

The information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation.

Methods

We propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tum

SUBMITTER: Sun K 

PROVIDER: S-EPMC11250309 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature