Best Poster Award MIDL 2019

During the 2nd edition of MIDL, Thomas de Bel won the Best Poster Award for his poster on stain-transforming cycle-consistent generative adversarial networks (cycleGAN) for improved segmentation of renal histopathology.

Color variations in digital histopathological slides due to differences in tissue processing or scanning techniques can negatively affect the performance of deep learning applications. Thomas de Bel et al applied cycleGAN for stain transformation between two centers and have adapted the orginical cycleGAN architecture for improved training stability and performance, generating high quality artifically stained images. The authors trained two segmentation networks for the analysis of renal tissue using single center data; one with tranformed images, and one without. Stain transformation proved to be beneficial for the segmentation performance on data sets from both centers, raising the Dice-coefficients from 0.36 to 0.85 and from 0.45 to 0.73. Read more about this work in the Proceeding of Machine Learning Research.

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