804 citations · 1.1k across the 5 of their papers we have counts for
5 papers
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos +30
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is highly subjective an…
Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images
Babak Ehteshami Bejnordi, Jimmy Linz, Ben Glass +6
Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, m…
Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images
Manan Shah, Christopher Rubadue, David Suster +1
Current analysis of tumor proliferation, the most salient prognostic biomarker for invasive breast cancer, is limited to subjective mitosis counting by pathologists in localized re…
Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method
Humayun Irshad, Eun-Yeong Oh, Daniel Schmolze +4
The assessment of protein expression in immunohistochemistry (IHC) images provides important diagnostic, prognostic and predictive information for guiding cancer diagnosis and ther…
Deep Learning for Identifying Metastatic Breast Cancer
Dayong Wang, Aditya Khosla, Rishab Gargeya +2
The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole s…