  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.
