Pretrained Vision Models for Predicting High-Risk Breast Cancer Stage

Bonaventure F. P. Dossou, Yeno Gbenou, Miglanche Ghomsi

Keywords: medical imaging, deep learning, breast cancer, pretrained computer vision models

TL;DR: Pretrained Vision Models for Predicting High-Risk Breast Cancer Stage

Abstract: 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. We also provide analyses of the results and explore pathways for better interpretability and generalization.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2303.10730/code)