A pixel and channel enhanced up-sampling module for biomedical image segmentation

Up-sampling operations are frequently utilized to recover the spatial resolution of feature maps in neural networks for segmentation task. However, current up-sampling methods, such as bilinear interpolation or deconvolution, do not fully consider the relationship of feature maps, which have negative impact on learning discriminative features for semantic segmentation. In this paper, we propose a pixel and channel enhanced up-sampling (PCE) module for low-resolution feature maps, aiming to use the relationship of adjacent pixels and channels for learning discriminative high-resolution feature maps. Specifically, the proposed up-sampling module includes two main operations: (1) increasing spatial resolution of feature maps with pixel shuffle and (2) recalibrating channel-wise high-resolution feature response. Our proposed up-sampling module could be integrated into CNN and Transformer segmentation architectures. Extensive experiments on three different modality datasets of biomedical images, including computed tomography (CT), magnetic resonance imaging (MRI) and micro-optical sectioning tomography images (MOST) demonstrate the proposed method could effectively improve the performance of representative segmentation models.