Towards Interpretable Attention Networks for Cervical Cancer Analysis.

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 explainable methods to explore and understand how the proposed models reach their classification decisions on multi-cell images which contain multiple cells. Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks to classify multiple cervical cells. Our aim is to provide interpretable deep learning models by comparing their explainability through the gradients visualization. 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 multiple cervical cells classification. This work highlights the benefits of attention networks to exploit relations and distributions within multi-cell images for cervical cancer analysis. Such an approach can assist clinicians in understanding a model's prediction by providing interpretable results.