Cargando…
BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. Howe...
Autores principales: | Su, Ziyu, Niazi, Muhammad Khalid Khan, Tavolara, Thomas E., Niu, Shuo, Tozbikian, Gary H., Wesolowski, Robert, Gurcan, Metin N. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072418/ https://www.ncbi.nlm.nih.gov/pubmed/37014891 http://dx.doi.org/10.1371/journal.pone.0283562 |
Ejemplares similares
-
Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels
por: Tavolara, Thomas E., et al.
Publicado: (2022) -
NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images
por: Sajjad, Usama, et al.
Publicado: (2023) -
A modular cGAN classification framework: Application to colorectal tumor detection
por: Tavolara, Thomas E., et al.
Publicado: (2019) -
Author Correction: A modular cGAN classification framework: Application to colorectal tumor detection
por: Tavolara, Thomas E., et al.
Publicado: (2020) -
Relationship between the Ki67 index and its area based approximation in breast cancer
por: Niazi, Muhammad Khalid Khan, et al.
Publicado: (2018)