UNesT: Local spatial representation learning with hierarchical transformer for efficient medical segmentation.

ABSTRACT: Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissue structures. To address such challen

SUBMITTER: Yu X 

PROVIDER: S-EPMC11229077 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature