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- W4330339901 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, and offer a winning solutionfootnote{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}" @default.
- W4330339901 created "2023-03-22" @default.
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- W4330339901 date "2023-03-19" @default.
- W4330339901 modified "2023-09-27" @default.
- W4330339901 title "Pretrained Vision Models for Predicting High-Risk Breast Cancer Stage" @default.
- W4330339901 doi "https://doi.org/10.48550/arxiv.2303.10730" @default.
- W4330339901 hasPublicationYear "2023" @default.
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