Bioinformatics (Oxford, England) · 2026
FMCerNet: Frequency-Aware Multi-Label Learning for Fine-Grained Cervical Cytology Screening.
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Zhou Linyun, Cai Qiuyu, Wang Xiaoling, Li Weihan, Yang Jian, Song Mingli, Zhang Jing, Zhou Feng, You Qihan, Wang Yudong, Feng Zunlei, Zhang Xiuming
Аннотация
MOTIVATION: Automated cytology screening is essential for early cervical cancer prevention, where gigapixel whole-slide images (WSIs) are typically partitioned into tiles for tile-level feature extraction and subsequent WSI-level diagnosis aggregation. However, existing tile-level methods remain constrained by limited input sizes, which frequently obscures sparse and morphologically subtle abnormal cells. Furthermore, these methods commonly formulate tile-level representation learning as a coarse single-label classification task, thereby failing to capture the complex co-occurrence patterns and morphological heterogeneity of cervical cytological abnormalities.
RESULTS: We propose the Frequency-Aware Multi-Label Learning Network (FMCerNet), a tile-level feature extractor equipped with a Large-Field Frequency-Enhanced ViT (LFreq-ViT) backbone and a Coarse-to-Fine multi-label classification (C2F) head. The former incorporates a Dual-Tree Complex Wavelet Transform module to efficiently extract frequency-domain features from large-scale tiles, whereas the latter enhances fine-grained tile representation decoding through hierarchical supervision. Experiments on both public and private datasets demonstrate that FMCerNet improves the accuracy of positive screening and fine-grained subtype classification while maintaining high inference speed.
AVAILABILITY AND IMPLEMENTATION: Code at https://github.com/imAzhou/FMCerNet and archived on Zenodo (doi:10.5281/zenodo.22886105).
SUPPLEMENTARY INFORMATION: Supplementary files are available with the manuscript.
RESULTS: We propose the Frequency-Aware Multi-Label Learning Network (FMCerNet), a tile-level feature extractor equipped with a Large-Field Frequency-Enhanced ViT (LFreq-ViT) backbone and a Coarse-to-Fine multi-label classification (C2F) head. The former incorporates a Dual-Tree Complex Wavelet Transform module to efficiently extract frequency-domain features from large-scale tiles, whereas the latter enhances fine-grained tile representation decoding through hierarchical supervision. Experiments on both public and private datasets demonstrate that FMCerNet improves the accuracy of positive screening and fine-grained subtype classification while maintaining high inference speed.
AVAILABILITY AND IMPLEMENTATION: Code at https://github.com/imAzhou/FMCerNet and archived on Zenodo (doi:10.5281/zenodo.22886105).
SUPPLEMENTARY INFORMATION: Supplementary files are available with the manuscript.