Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis

Aug 3, 2026

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Accurate hepatic steatosis assessment is crucial in liver transplantation, yet reliable diagnosis is constrained by limited training data and the need to protect sensitive patient information. The study develops hybrid quantum machine learning algorithms for classifying liver biopsy images, enabling experts to focus on complex cases while handling simpler ones quickly. A hybrid quantum neural network trained on real clinical data reaches 97% image classification accuracy, outperforming traditional approaches by 1.8%. Federated learning preserves privacy across multiple clients while maintaining over 90% accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Problem statement: steatosis accuracy and data privacy

Quantum machine learning and hybrid quantum–classical models

Study dataset and evaluation goals

Federated learning setup and compliance

Literature Review

arXiv :2311 .02402v2 [ cs .LG] 28 Mar 2024 Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis Luca Lusnig, 1, 2 Asel Sagingalieva, 1 Mikhail Surmach, 1 Tatjana Protasevich, 1 Ovidiu Michiu, 1 Joseph McLoughlin, 1 Christopher Mansell, 1 Graziano de’ Petris,3 Deborah Bonazza,4 Fabrizio Zanconati,4 Alexey Melnikov, 1 and Fabio Cavalli2 1 Terra Quantum AG, 9000 St. Gallen, Switzerland 2 Research Unit of Paleoradiology and Allied Sciences, LTS - SCIT, Azienda Sanitaria Universitaria Giuliana Isontina, 34149 Trieste, Italy 3 Data Protection Officer, Azienda Sanitaria Universitaria Giuliana Isontina, 34149 Trieste, Italy 4 Department of Medical, Surgical and Health Sciences, University of Trieste, Cattinara Academic Hospital, Trieste 34149, Italy 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. INTRODUCTION Please check the published version, which includes all the latest additions and corrections: Diagnostics 14(5):558, 2024, DOI: 10.3390/diagnostics14050558 In addressing the global challenge of determining liver viability for transplantation, this study tackles two main issues: the accuracy of hepatic steatosis diagnostics and the preservation of patient data privacy. The accurate classification of liver biopsy images is vital for assessing transplant viability, yet it is hampered by the substantial data requirements for training sophisticated machine learning models and the inherent privacy concerns associated with sensitive patient data. Federated learning (FL) offers a solution to these privacy concerns by enabling collaborative model training across multiple clients without centralizing sensitive data, thus adhering to data protection regulations such as the GDPR [1] . This approach is further underscored by upcoming regulations, like the EU AI Law [2], emphasizing the need for models that balance accuracy with privacy. Recent advancements in convolutional neural networks (CNNs) [3, 4] and the extension to residual neural networks (ResNet) [5] has shown promise in various pattern recognition tasks, including medical image analysis. The evolution of machine learning has seen the rise of quan- tum machine learning (QML), which combines quantum computing principles with classical techniques to potentially enhance model performance [6–9] . The integration of hybrid quantum–classical neural networks (HQNNs) into traditional CNN architectures promises impro

FAQs

Why is hepatic steatosis diagnosis important for liver transplantation?

Accurate steatosis classification is needed to assess transplant viability. The study links image classification performance directly to making more reliable transplantation decisions.

How does the proposed model improve classification accuracy for biopsy images?

It uses a hybrid quantum neural network combined with a ResNet-based approach to classify non-alcoholic liver steatosis. The reported results reach 97% accuracy.

How does federated learning address privacy concerns?

Federated learning enables collaborative model training across multiple clients without centralizing sensitive patient data. This supports privacy protections aligned with regulations such as GDPR while keeping accuracy above 90%.

Aug 3, 2026

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