BM-SMIL: A Breast Cancer Molecular Subtype Prediction Framework from H&E Slides with Self-supervised Pretraining and Multi-instance Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45087-7_9
Reference27 articles.
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3. Rawat, R.R., Ortega, I., Roy, P., et al.: Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&E images. Sci. Rep. 10(1), 1–13 (2020)
4. Jaber, M.I., Song, B., Taylor, C., et al.: A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival. Breast Cancer Res. 22(1), 1–10 (2020)
5. Liu, H., Xu, W.D., Shang, Z.H., et al.: Breast cancer molecular subtype prediction on pathological images with discriminative patch selection and multi-instance learning. Front. Oncol. 12, 858453 (2022)
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