Work

A cell comparative multiple instance learning network guided by image quality assessment for cervical whole slide image classification

Year: 2026

Type: article

Abstract: Early screening is essential for reducing the incidence and mortality of cervical cancer, and artificial intelligence-based analysis of whole slide images (WSIs) enables large-scale automated screening. However, existing methods often ignore image quality variations and inter-individual morphological differences, which limits their robustness in clinical settings. This study proposes a quality-aware cervical WSI classification framework that integrates image quality assessment with pathologist-inspired normal-abnormal cell comparison. A quality evaluation module filters unreliable patches, while a cell comparison and enhancement strategy enlarges the feature discrepancy between normal and abnormal cells to mitigate individual variability. Supervised contrastive learning further strengthens abnormal cell discrimination, and patch-level quality scores are incorporated into an attention-based multiple instance learning framework to guide WSI classification. Experiments on 2,434 WSIs from five medical institutions demonstrate that our method achieves superior performance in real-world scenarios, significantly outperforming state-of-the-art methods by 1.93% in average overall accuracy.

Source: iScience

DOI: 10.1016/j.isci.2026.115561

Authors: Lanlan Kang, Jian Wang, Yongjun He, Jian Qin

Institutions: Harbin University of Science and Technology, Anhui University of Technology, Harbin Institute of Technology

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,240