Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains

[1]  Erratum: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. , 2020, CA: a cancer journal for clinicians.

[2]  G. Wainrib,et al.  Deep learning-based classification of mesothelioma improves prediction of patient outcome , 2019, Nature Medicine.

[3]  Thomas J. Fuchs,et al.  Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.

[4]  Ron Kimmel,et al.  Artificial Intelligence Algorithms to Assess Hormonal Status From Tissue Microarrays in Patients With Breast Cancer , 2019, JAMA network open.

[5]  J. Boughey,et al.  Effect of Surgery Type on Time to Adjuvant Chemotherapy and Impact of Delay on Breast Cancer Survival: A National Cancer Database Analysis , 2019, Annals of Surgical Oncology.

[6]  N. Razavian,et al.  Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.

[7]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[8]  D. Ruderman,et al.  Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens , 2018, npj Breast Cancer.

[9]  Heather D. Couture,et al.  Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype , 2018, npj Breast Cancer.

[10]  Nico Karssemeijer,et al.  Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies , 2018, Modern Pathology.

[11]  Max Welling,et al.  Attention-based Deep Multiple Instance Learning , 2018, ICML.

[12]  D. Brat,et al.  Predicting cancer outcomes from histology and genomics using convolutional networks , 2017, Proceedings of the National Academy of Sciences.

[13]  Jian Huang,et al.  Treatment and survival outcomes of lobular carcinoma in situ of the breast: a SEER population based study , 2017, Oncotarget.

[14]  Graham W. Taylor,et al.  Improved Regularization of Convolutional Neural Networks with Cutout , 2017, ArXiv.

[15]  Ping Tang,et al.  Immunohistochemical Surrogates for Molecular Classification of Breast Carcinoma: A 2015 Update. , 2016, Archives of pathology & laboratory medicine.

[16]  Dayong Wang,et al.  Deep Learning for Identifying Metastatic Breast Cancer , 2016, ArXiv.

[17]  B. van Ginneken,et al.  Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis , 2016, Scientific Reports.

[18]  Abhinav Gupta,et al.  Training Region-Based Object Detectors with Online Hard Example Mining , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  S. Masood Breast Cancer Subtypes: Morphologic and Biologic Characterization , 2016, Women's health.

[20]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  A. Nassar,et al.  Clinical outcome in pleomorphic lobular carcinoma: a case-control study with comparison to classic invasive lobular carcinoma. , 2015, Annals of diagnostic pathology.

[22]  Trupti M. Kodinariya,et al.  Review on determining number of Cluster in K-Means Clustering , 2013 .

[23]  J. Nesland,et al.  Limitations of tissue microarrays compared with whole tissue sections in survival analysis. , 2010, Oncology letters.

[24]  M. Dowsett,et al.  American society of clinical oncology/college of american pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. , 2010, Journal of oncology practice.

[25]  Donald A Berry,et al.  NCCN Task Force Report: Estrogen Receptor and Progesterone Receptor Testing in Breast Cancer by Immunohistochemistry. , 2009, Journal of the National Comprehensive Cancer Network : JNCCN.

[26]  M. Greene,et al.  The role of HER2 in early breast cancer metastasis and the origins of resistance to HER2-targeted therapies. , 2009, Experimental and molecular pathology.

[27]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Sunil R Lakhani,et al.  Pleomorphic lobular carcinoma of the breast: molecular pathology and clinical impact. , 2009, Future oncology.

[29]  Jia Deng,et al.  A large-scale hierarchical image database , 2009, CVPR 2009.

[30]  A. Gown Current issues in ER and HER2 testing by IHC in breast cancer , 2008, Modern Pathology.

[31]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[32]  M. Rossing,et al.  Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients , 2007, Breast Cancer Research.

[33]  Ian O Ellis,et al.  Estrogen receptor-negative breast carcinomas: a review of morphology and immunophenotypical analysis , 2005, Modern Pathology.

[34]  D. Trask,et al.  Validation of Tissue Microarrays Using P53 Immunohistochemical Studies of Squamous Cell Carcinoma of the Larynx , 2003, Modern Pathology.

[35]  Z. Varga,et al.  Pleomorphic lobular carcinoma of the breast: its cell kinetics, expression of oncogenes and tumour suppressor genes compared with invasive ductal carcinomas and classical infiltrating lobular carcinomas , 2001, Histopathology.

[36]  M. Bonetti,et al.  Early start of adjuvant chemotherapy may improve treatment outcome for premenopausal breast cancer patients with tumors not expressing estrogen receptors. The International Breast Cancer Study Group. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[37]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[38]  P. Validire,et al.  Mammographically-detected ductal in situ carcinoma of the breast analyzed with a new classification. A study of 127 cases: correlation with estrogen and progesterone receptors, p53 and c-erbB-2 proteins, and proliferative activity. , 1994, Seminars in diagnostic pathology.

[39]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[40]  N. Otsu A threshold selection method from gray level histograms , 1979 .