MacNet: a mobile attention classification network combining convolutional neural network and transformer for the differentiation of cervical cancer

作者：Yi An, Yuanyuan Lei, Zhenxing Huang, Yu Liu, Meiyong Huang, Zhou Liu, Wenbo Li, Dong Liang, Wenting Huang, Zhanli Hu · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2024 · DOI：10.21037/qims-24-810 · 被引用次数：2 · 研究领域：AI in cancer detection、Advanced Neural Network Applications、Cervical Cancer and HPV Research

Background: Cervical cancer remains a critical global health issue, responsible for over 600,000 new cases and 300,000 deaths annually. Pathological imaging of cervical cancer is a crucial diagnostic tool. However, distinguishing specific areas of cellular differentiation remains challenging because of the lack of clear boundaries between cells at various stages of differentiation. To address the limitations of conventional clinical and deep learning (DL) methods, we developed a mobile attention classification network (MacNet) with multiscale features, aiming to increase the accuracy of differentiation classification and quantitatively analyze cervical cancer cell differentiation. Methods: We investigated the application of MacNet for classifying non-background images into 3 stages of cervical cancer differentiation. The feature maps are processed through the Mobile Convolution Neural Network with Mobile Attention (MCMA) module, which integrates mobile convolutional blocks and mobile attention blocks. MacNet harnesses the benefits of the image pyramid structure and self-attention mechanism, enabling multiscale feature extraction and emulation of clinical pathologist analysis. The final prediction is generated by the adaptive fusion module, which aggregates features into a unified output. Results: Comparative evaluations demonstrated that MacNet outperforms existing models. The proposed method achieved the best classification accuracy of 92.34% among all 7 DL-based models. Spe...