Authors
Linyun Zhou, Qiuyu Cai, Xiaoling Wang, Weihan Li, Jian Yang, Mingli Song, Jing Zhang, Feng Zhou, Qihan You, Yudong Wang, Zunlei Feng, Xiuming Zhang
Published in
Bioinformatics (Oxford, England). Oct 19, 2026. Epub Oct 19, 2026.
Abstract
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.
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.
Code at https://github.com/imAzhou/FMCerNet and archived on Zenodo (doi:10.5281/zenodo.22886105).
Supplementary files are available with the manuscript.
PMID:
42895261
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.
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