Wagner, Nicolas ; Fuchs, Moritz ; Tolkach, Yuri ; Mukhopadhyay, Anirban (2022)
Federated Stain Normalization for Computational Pathology.
25th International Conference on Medical Image Computing and Computer-Assisted Intervention. Singapore (18.09.2022-22.09.2022)
doi: 10.1007/978-3-031-16434-7_2
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Kurzbeschreibung (Abstract)

Although deep federated learning has received much attentionin recent years, progress has been made mainly in the context ofnatural images and barely for computational pathology. However, deep federated learning is an opportunity to create datasets that reflect the data diversity of many laboratories. Further, the effort of dataset construction can be divided among many. Unfortunately, existing algorithms cannot be easily applied to computational pathology since previous work presupposes that data distributions of laboratories must be similar. Thisis an unlikely assumption, mainly since different laboratories have different staining styles. As a solution, we propose BottleGAN, a generative model that can computationally align the staining styles of many laboratories and can be trained in a privacy-preserving manner to foster federated learning in computational pathology. We construct a heterogenic multi-institutional dataset based on the PESO segmentation dataset and improve the IOU by 42% compared to existing federated learning algorithms. An implementation of BottleGAN is available thttps://github.com/MECLabTUDA/BottleGAN.

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