  In the realm of liver transplantation, accurately determining hepatic
steatosis levels is crucial. Recognizing the essential need for improved
diagnostic precision, particularly for optimizing diagnosis time by swiftly
handling easy-to-solve cases and allowing the expert time to focus on more
complex cases, this study aims to develop cutting-edge algorithms that enhance
the classification of liver biopsy images. Additionally, the challenge of
maintaining data privacy arises when creating automated algorithmic solutions,
as sharing patient data between hospitals is restricted, further complicating
the development and validation process. This research tackles diagnostic
accuracy by leveraging novel techniques from the rapidly evolving field of
quantum machine learning, known for their superior generalization abilities.
Concurrently, it addresses privacy concerns through the implementation of
privacy-conscious collaborative machine learning with federated learning. We
introduce a hybrid quantum neural network model that leverages real-world
clinical data to assess non-alcoholic liver steatosis accurately. This model
achieves an image classification accuracy of 97%, surpassing traditional
methods by 1.8%. Moreover, by employing a federated learning approach that
allows data from different clients to be shared while ensuring privacy, we
maintain an accuracy rate exceeding 90%. This initiative marks a significant
step towards a scalable, collaborative, efficient, and dependable computational
framework that aids clinical pathologists in their daily diagnostic tasks.
