Analyzing domain shift when using additional data for the MICCAI KiTS23 Challenge
This addresses domain shift issues in medical imaging for researchers, but it is incremental as it compares existing techniques.
The paper tackled the problem of domain shift when using additional training data for 3D medical image segmentation, showing that histogram matching outperforms simple normalization in improving usability.
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.