no code implementations • 15 May 2019 • Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Xiaochun Zhao, Leandro Borba Moreira, Sirin Gandhi, Claudio Cavallo, Jennifer Eschbacher, Peter Nakaji, Mark C. Preul, Yezhou Yang
To improve the diagnostic quality of CLE, we used a micrograph of an H&E slide from a glioma tumor biopsy and image style transfer, a neural network method for integrating the content and style of two images.
no code implementations • 26 Apr 2018 • Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Michael Mooney, Jennifer Eschbacher, Peter Nakaji, Yezhou Yang, Mark C. Preul
We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models.
no code implementations • 25 Apr 2018 • Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Claudio Cavallo, Xiaochun Zhao, Sirin Gandhi, Leandro Borba Moreira, Jennifer Eschbacher, Peter Nakaji, Mark C. Preul, Yezhou Yang
To overcome this problem, we propose a Weakly-Supervised Learning (WSL)-based model for feature localization that trains on image-level annotations, and then localizes incidences of a class-of-interest in the test image.
no code implementations • 6 Jan 2018 • Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Nikolay Martirosyan, Jennifer Eschbacher, Peter Nakaji, Yezhou Yang, Mark C. Preul
Examining all the hundreds or thousands of images from a single case to discriminate diagnostic images from nondiagnostic ones can be tedious.
no code implementations • 10 Sep 2017 • Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Michael Mooney, Nikolay Martirosyan, Jennifer Eschbacher, Peter Nakaji, Mark C. Preul, Yezhou Yang
While manual examination of thousands of nondiagnostic images during surgery would be impractical, this creates an opportunity for a model to select diagnostic images for the pathologists or surgeon's review.