Abstract LB115: Unpaired image-to-image translation for unsupervised object segmentation in digital histopathology images

Nikola Andrejić, Milica Spasic, Igor Mihajlovic, Petra Milosavljević, Uroš Milivojević, Danilo Delibasic, Ivana Mikic, Sinisa Todorovic

Abstract: AI tools for image segmentation require large training datasets that are annotated with many examples of objects of interest, e.g. manual annotated cell nuclei for training a model for nuclear segmentation. Such public datasets exist for H&E stains and well performing models for that stain are therefore easy to train and are also available as open source. For other stains, repeating the task of manual labeling a sufficient number of images is expensive and time consuming. Instead of training a new model for each new dataset, we propose a domain adaptation approach, where new datasets are translated to the domain of the dataset for which a good model is already available. This is achieved by training a model for unpaired image-to-image translation, that retains the biological content of the image, but translates the ‘style’ between domains.