no code implementations • 6 Oct 2022 • Muhammad F. A. Chaudhary, Sarah E. Gerard, Gary E. Christensen, Christopher B. Cooper, Joyce D. Schroeder, Eric A. Hoffman, Joseph M. Reinhardt
To assess model generalizability beyond the development set biases, we evaluate our model on an out-of-distribution external validation set of 200 subjects.
1 code implementation • 20 Jul 2022 • Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Tolga Tasdizen
We extract snippets from the ET data by associating them with the dictation of keywords and use them to supervise the localization of specific abnormalities.
1 code implementation • 22 Dec 2021 • Ricardo Bigolin Lanfredi, Ambuj Arora, Trafton Drew, Joyce D. Schroeder, Tolga Tasdizen
The interpretability of medical image analysis models is considered a key research field.
no code implementations • 15 Oct 2021 • Muhammad F. A. Chaudhary, Sarah E. Gerard, Di Wang, Gary E. Christensen, Christopher B. Cooper, Joyce D. Schroeder, Eric A. Hoffman, Joseph M. Reinhardt
Once trained, the framework can be used as a registration-free method for predicting local tissue expansion.
1 code implementation • 29 Sep 2021 • Ricardo Bigolin Lanfredi, Mingyuan Zhang, William F. Auffermann, Jessica Chan, Phuong-Anh T. Duong, Vivek Srikumar, Trafton Drew, Joyce D. Schroeder, Tolga Tasdizen
Furthermore, a small subset of the data contains readings from all radiologists, allowing for the calculation of inter-rater scores.
1 code implementation • 10 Sep 2020 • Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Tolga Tasdizen
To evaluate the alignment with this direction after adversarial training, we apply a metric that uses generative adversarial networks to produce the smallest residual needed to change the class present in the image.
1 code implementation • 4 Jul 2020 • Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Clement Vachet, Tolga Tasdizen
The high complexity of deep learning models is associated with the difficulty of explaining what evidence they recognize as correlating with specific disease labels.
1 code implementation • 27 Aug 2019 • Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Clement Vachet, Tolga Tasdizen
We use a conditional generative adversarial network where the generator attempts to learn to shift the output of a regressor through creating disease effect maps that are added to the original images.