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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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Published on: October 28, 2018

An unsupervised style normalization method for cytopathology images.

Xihao Chen¹, Jingya Yu¹, Shenghua Cheng¹

¹Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, China.

Computational and Structural Biotechnology Journal

|July 21, 2021

Summary

This summary is machine-generated.

This study introduces an unsupervised method to normalize cytopathology image styles, improving automated analysis. The approach enhances model generalization by ensuring consistent image appearance across diverse datasets.

Keywords:

Cytopathology images Domain adversarial networks Generative adversarial learning Unsupervised image style normalization

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Area of Science:

Medical image analysis
Computational pathology
Artificial intelligence in healthcare

Background:

Variations in cytopathology image styles negatively impact the generalization of automated image analysis algorithms.
Standardizing image appearance is crucial for reliable diagnostic tools.

Purpose of the Study:

To propose an unsupervised method for normalizing cytopathology image styles.
To enhance the generalization ability of automated image analysis algorithms for cytopathology.

Main Methods:

A two-stage style normalization framework was developed, involving a style removal module and a domain adversarial style reconstruction module.
The method utilizes a color-encoding mask and per-pixel regression to maintain hue and structure consistency.
Intra-domain and inter-domain adversarial learning were employed for style consistency across different domains.

Main Results:

The proposed method demonstrated superior performance compared to existing unsupervised color normalization techniques on six cervical cell datasets.
Normalization significantly improved the recognition accuracy of lesion cells on previously unseen cytopathology images.

Conclusions:

The unsupervised style normalization method effectively addresses the challenge of diverse image styles in cytopathology.
This approach enhances model generalization, leading to more robust and accurate automated analysis of cytopathology images.