Using additional training data is known to improve the results, especially for medical image 3D segmentation where there is a lack of training material and the model needs to generalize well from few available data.

However, the new data could have been acquired using other instruments and preprocessed such its distribution is significantly different from the original training data.

Therefore, we study techniques which ameliorate domain shift during training so that the additional data becomes better usable for preprocessing and training together with the original data.

Our results show that transforming the additional data using histogram matching has better results than using simple normalization.

This is one of the scientific articles published by one or more synbrAIn collaborators and data scientists.

If you would like to learn more, read the full article here.