Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer
Fig 2
The T-GAN-D robustly stratifies low and high risk breast cancer patients.
(A) Workflow of the data processing, including the schematics of the generator network and its adversary, the discriminator network. Together these result in an AC-WGAN-GP architecture. After the conversion of patient transcriptome profiles into images, 4/5 of the MB dataset was used to train the GAN’s discriminator. After 1000 epochs, the trained discriminator was used as a standalone classifier to separate the remaining 1/5 patients of the dataset into low and high risk categories. (B) Kaplan-Meier curves separating low vs. high risk patients as predicted with the T-GAN-D (iteration 1 of the 5-fold CV shown as representative). (C) Kaplan-Meier curves generated pooling the category predictions obtained for all patients of the MB dataset after five independent CV runs. (D) Separation of low vs. high risk patients predicted with a classical CNN on the same subset used in B and (E) comparison obtained pooling the predictions of five independent CV runs. The area between the curves (ABC) between Low risk (blue dashed line) and Predicted low risk (solid blue line), Predicted low risk and Predicted high risk (solid red line), Predicted high risk and High risk groups (dashed red line) are shown top to bottom in D and E.