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}

本研究使用数字病理（乳腺活检）图像的Nightingale开放科学数据集，利用预先训练的计算机视觉模型进行乳腺癌阶段预测，最终我们发现集成模型的预测性能更好，具有胜出的优势；此外，我们提供结果分析并探索更好的解释和泛化途径。

预训练视觉模型用于预测高风险乳腺癌分期