FMCerNet: Frequency-Aware Multi-Label Learning for Fine-Grained Cervical Cytology Screening

Clicks: 1
ID: 332906
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #956 of 957 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 957 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
openalex_W7222943549 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
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
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag759
URL
https://doi.org/10.1093/bioinformatics/btag759
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.