现代时代，结直肠癌是一种常见疾病，其中大多数病例由息肉引起。因此，医学图像分割领域对息肉分割的关注度很高。近年来，基于U-Net网络的变种网络已经在息肉分割挑战中展示出良好的分割效果。本文提出了一种称为CFHA-Net的息肉分割模型，该模型结合了交叉尺度特征融合策略和混合注意机制。受到特征学习的启发，编码器单元引入了交叉尺度上下文融合（CCF）模块，该模块执行跨层特征融合，增强不同尺度的特征信息。通过提出的三重混合注意（THA）模块对跳跃连接进行优化，汇聚了来自三个方向的空间和通道注意特征，改善了特征之间的远距离依赖性，有助于识别随后的息肉病变边界。此外，在瓶颈层增加了密集感知特征融合（DFF）模块，该模块组合了密集连接和多感受野融合模块，以捕获更全面的上下文信息。此外，提出了混合池化（HP）模块和混合上采样（HU）模块，以帮助分割网络获取更多上下文特征。针对息肉分割的三个典型数据集（CVC-ClinicDB、Kvasir-SEG、EndoTect）进行了一系列实验，以评估所提出的CFHA-Net的有效性和泛化性。实验结果证明了所提方法的有效性和泛化性，许多性能指标超过了相关先进分割网络。因此，所提出的CFHA-Net可能是医学图像分析中解决息肉分割挑战的有希望的解决方案。所提出的CFHA-Net的源代码可在https://github.com/CXzhai/CFHA-Net.git获得。版权所有©2023 Elsevier Ltd. 保留所有权利。

Colorectal cancer is a prevalent disease in modern times, with most cases being caused by polyps. Therefore, the segmentation of polyps has garnered significant attention in the field of medical image segmentation. In recent years, the variant network derived from the U-Net network has demonstrated a good segmentation effect on polyp segmentation challenges. In this paper, a polyp segmentation model, called CFHA-Net, is proposed, that combines a cross-scale feature fusion strategy and a hybrid attention mechanism. Inspired by feature learning, the encoder unit incorporates a cross-scale context fusion (CCF) module that performs cross-layer feature fusion and enhances the feature information of different scales. The skip connection is optimized by proposed triple hybrid attention (THA) module that aggregates spatial and channel attention features from three directions to improve the long-range dependence between features and help identify subsequent polyp lesion boundaries. Additionally, a dense-receptive feature fusion (DFF) module, which combines dense connections and multi-receptive field fusion modules, is added at the bottleneck layer to capture more comprehensive context information. Furthermore, a hybrid pooling (HP) module and a hybrid upsampling (HU) module are proposed to help the segmentation network acquire more contextual features. A series of experiments have been conducted on three typical datasets for polyp segmentation (CVC-ClinicDB, Kvasir-SEG, EndoTect) to evaluate the effectiveness and generalization of the proposed CFHA-Net. The experimental results demonstrate the validity and generalization of the proposed method, with many performance metrics surpassing those of related advanced segmentation networks. Therefore, proposed CFHA-Net could present a promising solution to the challenges of polyp segmentation in medical image analysis. The source code of proposed CFHA-Net is available at https://github.com/CXzhai/CFHA-Net.git.Copyright © 2023 Elsevier Ltd. All rights reserved.