Towards Interpretable Attention Networks for Cervical Cancer Analysis

TL;DR

This paper evaluates and compares deep learning and attention-based models for cervical cancer image classification, emphasizing interpretability and the importance of multi-cell images over single-cell analysis.

Contribution

It introduces interpretable attention-based models, especially residual channel attention, for classifying multi-cell cervical images, enhancing explainability and effectiveness.

Findings

Residual channel attention model effectively extracts features from cell groups.

Multi-cell images improve classification accuracy over single-cell images.

Attention mechanisms enhance interpretability of cervical cancer models.

Abstract

Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals, including analysis of cervical cancer. Many previous works focus on the analysis of isolated cervical cells, or do not offer sufficient methods to explain and understand how the proposed models reach their classification decisions on multi-cell images. Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks for the classification of images of multiple cervical cells. As we aim to provide interpretable deep learning models to address this task, we also compare their explainability through the visualization of their gradients. We demonstrate the importance of using images that contain multiple cells over using isolated single-cell images. We show the effectiveness of the residual channel attention model for extracting important features from a group of cells, and demonstrate this model's efficiency for this classification task. This work highlights the benefits of channel attention mechanisms in analyzing multiple-cell images for potential relations and distributions within a group of cells. It also provides interpretable models to address the classification of cervical cells.