Multi-scale window transformer for cervical cytopathology image recognition

Jiaxiang Yi; Xiuli Liu; Shenghua Cheng; Li Chen; Shaoqun Zeng

doi:10.1016/j.csbj.2024.04.028

Multi-scale window transformer for cervical cytopathology image recognition

Comput Struct Biotechnol J. 2024 Apr 16:24:314-321. doi: 10.1016/j.csbj.2024.04.028. eCollection 2024 Dec.

Authors

Jiaxiang Yi¹, Xiuli Liu¹, Shenghua Cheng², Li Chen³, Shaoqun Zeng¹

Affiliations

¹ Britton Chance Center and MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.
² School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
³ Department of Clinical Laboratory, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Abstract

Cervical cancer is a major global health issue, particularly in developing countries where access to healthcare is limited. Early detection of pre-cancerous lesions is crucial for successful treatment and reducing mortality rates. However, traditional screening and diagnostic processes require cytopathology doctors to manually interpret a huge number of cells, which is time-consuming, costly, and prone to human experiences. In this paper, we propose a Multi-scale Window Transformer (MWT) for cervical cytopathology image recognition. We design multi-scale window multi-head self-attention (MW-MSA) to simultaneously integrate cell features of different scales. Small window self-attention is used to extract local cell detail features, and large window self-attention aims to integrate features from smaller-scale window attention to achieve window-to-window information interaction. Our design enables long-range feature integration but avoids whole image self-attention (SA) in ViT or twice local window SA in Swin Transformer. We find convolutional feed-forward networks (CFFN) are more efficient than original MLP-based FFN for representing cytopathology images. Our overall model adopts a pyramid architecture. We establish two multi-center cervical cell classification datasets of two-category 192,123 images and four-category 174,138 images. Extensive experiments demonstrate that our MWT outperforms state-of-the-art general classification networks and specialized classifiers for cytopathology images in the internal and external test sets. The results on large-scale datasets prove the effectiveness and generalization of our proposed model. Our work provides a reliable cytopathology image recognition method and helps establish computer-aided screening for cervical cancer. Our code is available at https://github.com/nmyz669/MWT, and our web service tool can be accessed at https://huggingface.co/spaces/nmyz/MWTdemo.

Keywords: Cervical cancer screening; Convolutional feed-forward network; Cytopathology image recognition; Multi-scale window transformer.