Deep learning provides a powerful data-driven solution to a wide range of imaging tasks, from data acquisition to image interpretation. To train these deep and highly nonlinear models, a well labeled and very large dataset is typically required. However, data with accurate labels are difficult, sometimes impossible, and expensive to collect. Without enough data, the learned model will be highly biased and unable to generalize. In the worst case, the application of the deep model may result in misdiagnosis and improper patient management. Thus, we propose NoiseFlow, a solution to reduce the dependency of deep learning solutions on real data through noise-driven training.
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