Federated Learning on Medical Data: Collaborative Research Project with ZEISS

May 1, 2021

Federated Learning in Opthalmology

Medical data is highly sensitive, and its centralization is often not possible. This can inhibit the development of machine learning models because traditional approaches rely on large centralized data sets for model training and evaluation.

In a joint effort with researchers from the ZEISS Group and the Carl Zeiss Meditec AG, we applied our decentralized approach to carry out anomaly detection in OCT images taken of eyes.

ZEISS OCT scanner
ZEISS OCT scanner, type CIRRUS HD-OCT 5000, image source: Carl Zeiss Meditec Inc.
Share models instead of raw data

In the project, we apply a paradigm shift in machine learning. Instead of transferring large data sets, we transfer only the individual models. These models are trained locally on the edge and then combined centrally into a more powerful model. More information about our Decentralized Machine Learning approach can be found here: Edge AI.

Model exchange while the OCT data stays local - using prenode mlx
Model exchange while the OCT data stays local - using prenode mlx
ARVO Conference on Ophthalmology Research

The Association for Research in Vision and Ophthalmology (ARVO) hosts their annual meeting in May 2021. We are proud to take part in this event and present an evaluation of the described Federated Learning approach on eye data from OCT scans to professionals in the field.

Authors: Robin Hirt, Christian Kungel, Caroline Dieterich, Gary Lee, Dominik Fischer, Niranchana Manivannan, Aditya Nair, Hugang Ren, Sophia Yu and Alexander Ulrich.

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