PUSHING COMPUTER VISION BOUNDARIES TO 50K AND BEYOND

Fast-forwarding to today, by using a more complex model and SambaNova’s Reconfigurable Dataflow Architecture™, an 89.4% AUC is achieved on the same data set. This boost in model performance is the combined result of using a more sophisticated model architecture and the fact that SambaNova’s Reconfigurable Dataflow Units™ (RDUs) can take large images as input that other chips cannot. As we support even larger image sizes in the future, we can expect the AUC percentage to further increase, which could potentially bring accurate, economic, and universal MSI status testing to all patients.

To demonstrate the effect of using larger images as input, we compare the AUC achieved by training a model using 5760x5760 images as the input versus that achieved by two other models trained using 512x512 images as the input (see next section for details). These models use the same advanced model architecture (RescaleNet50) and are run on the same hardware (RDUs). The result shows that input image size has a significant impact on AUC.