Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images
This work addresses privacy and data access issues in medical imaging for hospitals and researchers, but it is incremental as it combines existing methods in a new application.
The authors tackled the problem of data scarcity and privacy in histopathology by generating synthetic colorectal cancer images using vision Transformers and GANs, which improved classification accuracy by 5% when used for data augmentation, and replicated this performance in a federated learning setup with Kubernetes to simulate distributed hospital data without direct sharing.
In the field of deep learning, large architectures often obtain the best performance for many tasks, but also require massive datasets. In the histological domain, tissue images are expensive to obtain and constitute sensitive medical information, raising concerns about data scarcity and privacy. Vision Transformers are state-of-the-art computer vision models that have proven helpful in many tasks, including image classification. In this work, we combine vision Transformers with generative adversarial networks to generate histopathological images related to colorectal cancer and test their quality by augmenting a training dataset, leading to improved classification accuracy. Then, we replicate this performance using the federated learning technique and a realistic Kubernetes setup with multiple nodes, simulating a scenario where the training dataset is split among several hospitals unable to share their information directly due to privacy concerns.