Towards Interpretable Attention Networks for Cervical Cancer Analysis

Area of Science:

• Medical Imaging
• Deep Learning
• Computational Pathology

Background:

• Automated analysis of medical images, including cervical cancer screening, has advanced with deep learning.
• Existing methods often analyze isolated cells or lack explainability for multi-cell image classification.

Purpose of the Study:

• To evaluate state-of-the-art deep learning and attention-based models for classifying multiple cervical cells.
• To provide interpretable deep learning models for cervical cancer analysis using gradient visualization.
• To highlight the advantages of multi-cell image analysis over single-cell analysis.

Main Methods:

• Comparison of various deep learning models and attention-based frameworks.
• Classification of multiple cervical cells using deep learning.
• Explainability assessment via gradient visualization.
• Evaluation of a residual channel attention model.

Main Results:

• Multi-cell cervical images are more informative than isolated single-cell images for classification.
• The residual channel attention model effectively extracts features from groups of cells.
• This model demonstrates efficiency in classifying multiple cervical cells.

Conclusions:

• Attention networks are beneficial for analyzing relationships within multi-cell images for cervical cancer.
• Interpretable deep learning models, like the attention-based approach, can assist clinicians in understanding predictions.
• The study emphasizes the importance of context from multi-cell images in automated cervical cancer analysis.