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Cell comparative learning: A cervical cytopathology whole slide image classification method using normal and abnormal

Jian Qin¹, Yongjun He², Yiqin Liang³

¹School of Computer Science and Technology, Anhui University of Technology, Maanshan, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|August 31, 2024

Summary

This study introduces a new computer-assisted method for cervical cancer screening. The approach mimics pathologist comparisons of normal and abnormal cells, achieving pathologist-level accuracy in whole slide image analysis.

Keywords:

Cervical cytopathology image Deep learning Multiple instance learning Whole slide images classification

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Area of Science:

Digital Pathology
Computational Biology
Oncology

Background:

Automated cervical cancer screening using computer-assisted diagnosis shows promise for improving accessibility and reducing costs.
Current classification performance on whole slide images (WSIs) is limited by patient-specific variations.
Pathologists enhance screening precision by comparing abnormal cells with normal cells within the same WSI.

Purpose of the Study:

To develop a novel cervical cell comparative learning method for improved automated screening.
To leverage pathologist knowledge to differentiate between normal and abnormal cells within WSIs.
To enhance the precision and reliability of computer-assisted cervical cancer diagnosis.

Main Methods:

Utilized two pre-trained YOLOX models for detecting normal and abnormal cervical cells in WSIs.
Employed a self-supervised model for extracting cell features.
Integrated a Transformer encoder for fusing cell features and generating WSI instance embeddings.
Applied attention-based multi-instance learning for final classification.

Main Results:

Achieved an Area Under the Curve (AUC) of 0.9319 with the proposed method.
Demonstrated performance comparable to that of professional pathologists.
Indicated significant potential for clinical application in cervical cancer screening.

Conclusions:

The novel comparative learning method effectively addresses patient-specific variations in WSI analysis.
The approach successfully mimics expert pathologist comparative analysis for improved diagnostic accuracy.
This method holds significant promise for enhancing the efficiency and reliability of automated cervical cancer screening.