no code implementations • 3 Dec 2021 • Indrani Bhattacharya, David S. Lim, Han Lin Aung, Xingchen Liu, Arun Seetharaman, Christian A. Kunder, Wei Shao, Simon J. C. Soerensen, Richard E. Fan, Pejman Ghanouni, Katherine J. To'o, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu
Our experiments show that (1) radiologist labels and models trained with them can miss cancers, or underestimate cancer extent, (2) digital pathologist labels and models trained with them have high concordance with pathologist labels, and (3) models trained with digital pathologist labels achieve the best performance in prostate cancer detection in two different cohorts with different disease distributions, irrespective of the model architecture used.
no code implementations • 31 Jul 2020 • Indrani Bhattacharya, Arun Seetharaman, Wei Shao, Rewa Sood, Christian A. Kunder, Richard E. Fan, Simon John Christoph Soerensen, Jeffrey B. Wang, Pejman Ghanouni, Nikola C. Teslovich, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu
First, the model learns MRI signatures of cancer that are correlated with corresponding histopathology features using Common Representation Learning.