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Abstract #4737

Conditional adversarial network for segmentation with simple loss function

Andre Maximo1 and Chitresh Bhushan2

1GE Healthcare, Rio de Janeiro, Brazil, 2GE Global Research, Niskayuna, NY, United States

Most deep-learning approaches require defining a loss function that is appropriate for the task. The choice of the loss function generally substantially affects the accuracy of the trained model and often requires hand-tuning. For example, some segmentation tasks work well with Dice loss while other work well with mean squared error (MSE). In this work we show how conditional adversarial network (cGAN) can be used to avoid defining a specialized loss function for each task and, instead, use a simple approach to achieve comparable or even superior results in context of segmentation of MRI images.

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