DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation.
Journal:
Computer methods and programs in biomedicine
Published Date:
Nov 29, 2021
Abstract
BACKGROUND AND OBJECTIVE: Segmentation is a key step in biomedical image analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly applied in the field of medical image processing; however, standard models still have some drawbacks. Due to the significant loss of spatial information at the coding stage, it is often difficult to restore the details of low-level visual features using simple deconvolution, and the generated feature maps are sparse, which results in performance degradation. This prompted us to study whether it is possible to better preserve the deep feature information of the image in order to solve the sparsity problem of image segmentation models.