Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
出版年份 2020 全文链接
标题
Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
作者
关键词
-
出版物
Nature Communications
Volume 11, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2020-11-16
DOI
10.1038/s41467-020-19334-3
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Effect of Surgery Type on Time to Adjuvant Chemotherapy and Impact of Delay on Breast Cancer Survival: A National Cancer Database Analysis
- (2019) Amanda R. Kupstas et al. ANNALS OF SURGICAL ONCOLOGY
- Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
- (2019) Gabriele Campanella et al. NATURE MEDICINE
- Deep learning-based classification of mesothelioma improves prediction of patient outcome
- (2019) Pierre Courtiol et al. NATURE MEDICINE
- Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies
- (2018) Babak Ehteshami Bejnordi et al. MODERN PATHOLOGY
- Predicting cancer outcomes from histology and genomics using convolutional networks
- (2018) Pooya Mobadersany et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- (2018) Freddie Bray et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
- (2018) Nicolas Coudray et al. NATURE MEDICINE
- Treatment and survival outcomes of lobular carcinoma in situ of the breast: a SEER population based study
- (2017) Pu Cheng et al. Oncotarget
- NCCN Task Force Report: Estrogen Receptor and Progesterone Receptor Testing in Breast Cancer by Immunohistochemistry
- (2017) D. Craig Allred et al. Journal of the National Comprehensive Cancer Network
- Immunohistochemical Surrogates for Molecular Classification of Breast Carcinoma: A 2015 Update
- (2016) Ping Tang et al. ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE
- Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
- (2016) Geert Litjens et al. Scientific Reports
- Clinical outcome in pleomorphic lobular carcinoma: a case-control study with comparison to classic invasive lobular carcinoma
- (2015) Sonia Narendra et al. Annals of Diagnostic Pathology
- Limitations of tissue microarrays compared with whole tissue sections in survival analysis
- (2010) M. HAYSAM KHOUJA et al. Oncology Letters
- American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Immunohistochemical Testing of Estrogen and Progesterone Receptors in Breast Cancer
- (2010) M. Elizabeth H. Hammond et al. Journal of Oncology Practice
- The role of HER2 in early breast cancer metastasis and the origins of resistance to HER2-targeted therapies
- (2009) Jaclyn A. Freudenberg et al. EXPERIMENTAL AND MOLECULAR PATHOLOGY
- Pleomorphic lobular carcinoma of the breast: molecular pathology and clinical impact
- (2009) Ana-Cristina Vargas et al. Future Oncology
- Current issues in ER and HER2 testing by IHC in breast cancer
- (2008) Allen M Gown MODERN PATHOLOGY
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started