Application of deep learning and XGBoost in predicting pathological staging of breast cancer MR images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-023-05797-w.pdf
Reference37 articles.
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3. Chen L, Chen Y, Xie Z et al (2022) Comparison of immunohistochemistry and RT-qPCR for assessing ER, PR, HER2, and Ki67 and evaluating subtypes in patients with breast cancer. Breast Cancer Res Treat 194(3):517–529
4. Julca I ,Mutwil-Anderwald D ,Manoj V et al (2023) Genomic,transcriptomic,and metabolomic analysis of Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites. J Integrative Plant Biolo 65(06):1442–1466
5. Movahedpour A, Ahmadi K, Taheri-Anganeh M et al (2022) Designing a humanized immunotoxin based on HER2 specific scFv and DFF40 toxin against breast cancer: an in-silico study. Int J Pept Res Ther 28(5):1–11
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