Holistic and Historical Instance Comparison for Cervical Cell Detection

TL;DR

This paper introduces a novel holistic and historical instance comparison method for cervical cell detection in Pap smear images, addressing cell class ambiguity and imbalance issues to improve detection accuracy.

Contribution

It proposes a dual comparison scheme with a memory bank to enhance cell discrimination, especially for minor classes, in cervical cytology image analysis.

Findings

Improved detection accuracy on large-scale datasets.

Effective discrimination of subtle cell class differences.

Enhanced detection of minor cell categories.

Abstract

Cytology screening from Papanicolaou (Pap) smears is a common and effective tool for the preventive clinical management of cervical cancer, where abnormal cell detection from whole slide images serves as the foundation for reporting cervical cytology. However, cervical cell detection remains challenging due to 1) hazily-defined cell types (e.g., ASC-US) with subtle morphological discrepancies caused by the dynamic cancerization process, i.e., cell class ambiguity, and 2) imbalanced class distributions of clinical data may cause missed detection, especially for minor categories, i.e., cell class imbalance. To this end, we propose a holistic and historical instance comparison approach for cervical cell detection. Specifically, we first develop a holistic instance comparison scheme enforcing both RoI-level and class-level cell discrimination. This coarse-to-fine cell comparison encourages the model to learn foreground-distinguishable and class-wise representations. To emphatically improve the distinguishability of minor classes, we then introduce a historical instance comparison scheme with a confident sample selection-based memory bank, which involves comparing current embeddings with historical embeddings for better cell instance discrimination. Extensive experiments and analysis on two large-scale cytology datasets including 42,592 and 114,513 cervical cells demonstrate the effectiveness of our method. The code is available at https://github.com/hjiangaz/HERO.