1 code implementation • 1 Aug 2023 • Kasra Naftchi-Ardebili, Karanpartap Singh, Reza Pourabolghasem, Pejman Ghanouni, Gerald R. Popelka, Kim Butts Pauly
To overcome these challenges, we propose a generative adversarial network (SkullGAN) to create large datasets of synthetic skull CT slices, geared towards training models for transcranial ultrasound.
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 • 23 Jun 2021 • Wei Shao, Indrani Bhattacharya, Simon J. C. Soerensen, Christian A. Kunder, Jeffrey B. Wang, Richard E. Fan, Pejman Ghanouni, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu
Cancer labels achieved by image registration can be used to improve radiologists' interpretation of MRI by training deep learning models for early detection of prostate cancer.
1 code implementation • Medical Image Analysis 2021 • Rewa R. Sood, Wei Shao, Christian Kunder, Nikola C. Teslovich, Jeffrey B. Wang, Simon J.C. Soerensen, Nikhil Madhuripan, Anugayathri Jawahar, James D. Brooks, Pejman Ghanouni, Richard E. Fan, Geoffrey A. Sonn, Mirabela Rusu
Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI.
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.