Before revision:   To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty .
After revision:  All neural networks intended for clinical practice need to be robust to variation in data caused by differences in imaging equipment, sample preparation and patient populations. To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty .