  Cancer is increasingly a global health issue. Seconding cardiovascular
diseases, cancers are the second biggest cause of death in the world with
millions of people succumbing to the disease every year. According to the World
Health Organization (WHO) report, by the end of 2020, more than 7.8 million
women have been diagnosed with breast cancer, making it the world's most
prevalent cancer. In this paper, using the Nightingale Open Science dataset of
digital pathology (breast biopsy) images, we leverage the capabilities of
pre-trained computer vision models for the breast cancer stage prediction task.
While individual models achieve decent performances, we find out that the
predictions of an ensemble model are more efficient, and offer a winning
solution\footnote{https://www.nightingalescience.org/updates/hbc1-results}. We
also provide analyses of the results and explore pathways for better
interpretability and generalization. Our code is open-source at
\url{https://github.com/bonaventuredossou/nightingale_winning_solution}
