Active Learning Guided Federated Online Adaptation: Applications in
  Medical Image Segmentation
Md Shazid Islam Sayak Nag Arindam Dutta Miraj Ahmed Fahim Faisal Niloy Amit K. Roy-Chowdhury
Data privacy, storage, and distribution shifts are major bottlenecks in
medical image analysis. Data cannot be shared across patients, physicians, and
facilities due to privacy concerns, usually requiring each patient's data to be
analyzed in a discreet setting at a near real-time pace. However, one would
like to take advantage of the accumulated knowledge across healthcare
facilities as the computational systems analyze data of more and more patients
while incorporating feedback provided by physicians to improve accuracy.
Motivated by these, we propose a method for medical image segmentation that
adapts to each incoming data batch (online adaptation), incorporates physician
feedback through active learning, and assimilates knowledge across facilities
in a federated setup. Combining an online adaptation scheme at test time with
an efficient sampling strategy with budgeted annotation helps bridge the gap
between the source and the incoming stream of target domain data. A federated
setup allows collaborative aggregation of knowledge across distinct distributed
models without needing to share the data across different models. This
facilitates the improvement of performance over time by accumulating knowledge
across users. Towards achieving these goals, we propose a computationally
amicable, privacy-preserving image segmentation technique \textbf{DrFRODA} that
uses federated learning to adapt the model in an online manner with feedback
from doctors in the loop. Our experiments on publicly available datasets show
that the proposed distributed active learning-based online adaptation method
outperforms unsupervised online adaptation methods and shows competitive
results with offline active learning-based adaptation methods.