  Inspired by the success of Transformers in Computer vision, Transformers have
been widely investigated for medical imaging segmentation. However, most of
Transformer architecture are using the recent transformer architectures as
encoder or as parallel encoder with the CNN encoder. In this paper, we
introduce a novel hybrid CNN-Transformer segmentation architecture
(PAG-TransYnet) designed for efficiently building a strong CNN-Transformer
encoder. Our approach exploits attention gates within a Dual Pyramid hybrid
encoder. The contributions of this methodology can be summarized into three key
aspects: (i) the utilization of Pyramid input for highlighting the prominent
features at different scales, (ii) the incorporation of a PVT transformer to
capture long-range dependencies across various resolutions, and (iii) the
implementation of a Dual-Attention Gate mechanism for effectively fusing
prominent features from both CNN and Transformer branches. Through
comprehensive evaluation across different segmentation tasks including:
abdominal multi-organs segmentation, infection segmentation (Covid-19 and Bone
Metastasis), microscopic tissues segmentation (Gland and Nucleus). The proposed
approach demonstrates state-of-the-art performance and exhibits remarkable
generalization capabilities. This research represents a significant advancement
towards addressing the pressing need for efficient and adaptable segmentation
solutions in medical imaging applications.
