  Feature pyramids have been widely adopted in convolutional neural networks
(CNNs) and transformers for tasks like medical image segmentation and object
detection. However, the currently existing models generally focus on the
Encoder-side Transformer to extract features, from which decoder improvement
can bring further potential with well-designed architecture. We propose
CFPFormer, a novel decoder block that integrates feature pyramids and
transformers. Specifically, by leveraging patch embedding, cross-layer feature
concatenation, and Gaussian attention mechanisms, CFPFormer enhances feature
extraction capabilities while promoting generalization across diverse tasks.
Benefiting from Transformer structure and U-shaped Connections, our introduced
model gains the ability to capture long-range dependencies and effectively
up-sample feature maps. Our model achieves superior performance in detecting
small objects compared to existing methods. We evaluate CFPFormer on medical
image segmentation datasets and object detection benchmarks (VOC 2007, VOC2012,
MS-COCO), demonstrating its effectiveness and versatility. On the ACDC
Post-2017-MICCAI-Challenge online test set, our model reaches exceptionally
impressive accuracy, and performed well compared with the original decoder
setting in Synapse multi-organ segmentation dataset.
