  The automated analysis of medical images is currently limited by technical
and biological noise and bias. The same source tissue can be represented by
vastly different images if the image acquisition or processing protocols vary.
For an image analysis pipeline, it is crucial to compensate such biases to
avoid misinterpretations. Here, we evaluate, compare, and improve existing
generative model architectures to overcome domain shifts for immunofluorescence
(IF) and Hematoxylin and Eosin (H&E) stained microscopy images. To determine
the performance of the generative models, the original and transformed images
were segmented or classified by deep neural networks that were trained only on
images of the target bias. In the scope of our analysis, U-Net cycleGANs
trained with an additional identity and an MS-SSIM-based loss and Fixed-Point
GANs trained with an additional structure loss led to the best results for the
IF and H&E stained samples, respectively. Adapting the bias of the samples
significantly improved the pixel-level segmentation for human kidney glomeruli
and podocytes and improved the classification accuracy for human prostate
biopsies by up to 14%.
