  Deep Learning-based computational pathology algorithms have demonstrated
profound ability to excel in a wide array of tasks that range from
characterization of well known morphological phenotypes to predicting
non-human-identifiable features from histology such as molecular alterations.
However, the development of robust, adaptable, and accurate deep learning-based
models often rely on the collection and time-costly curation large high-quality
annotated training data that should ideally come from diverse sources and
patient populations to cater for the heterogeneity that exists in such
datasets. Multi-centric and collaborative integration of medical data across
multiple institutions can naturally help overcome this challenge and boost the
model performance but is limited by privacy concerns amongst other difficulties
that may arise in the complex data sharing process as models scale towards
using hundreds of thousands of gigapixel whole slide images. In this paper, we
introduce privacy-preserving federated learning for gigapixel whole slide
images in computational pathology using weakly-supervised attention multiple
instance learning and differential privacy. We evaluated our approach on two
different diagnostic problems using thousands of histology whole slide images
with only slide-level labels. Additionally, we present a weakly-supervised
learning framework for survival prediction and patient stratification from
whole slide images and demonstrate its effectiveness in a federated setting.
Our results show that using federated learning, we can effectively develop
accurate weakly supervised deep learning models from distributed data silos
without direct data sharing and its associated complexities, while also
preserving differential privacy using randomized noise generation.
