Citation: Lusnig, L.; Sagingalieva, A.; Surmach, M.; Protasevich, T.; Michiu, O.; McLoughlin, J.; Mansell, C.; de’ Petris, G.; Bonazza, D.; Zanconati, F.; et al. Hybrid Quantum Image Classification and Federated Learning for Hepatic Steatosis Diagnosis. Diagnostics 2024, 14, 558. https://doi.org/10.3390/ diagnostics14050558 Academic Editor: Dechang Chen Received: 10 January 2024 Revised: 17 February 2024 Accepted: 26 February 2024 Published: 6 March 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). diagnostics Article 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 Cavalli 2, * 1 Terra Quantum AG, 9000 St. Gallen, Switzerland; jm@terraquantum.swiss (J.M.); cm@terraquantum.swiss (C.M.); ame@terraquantum.swiss (A.M.) 2 Research Unit of Paleoradiology and Allied Sciences, Laboratorio di Telematica Sanitaria-Struttura Complessa Informatica e Telecomunicazioni, Azienda Sanitaria Universitaria Giuliana Isontina, 34149 Trieste, Italy 3 Laboratorio di Telematica Sanitaria-Struttura Complessa Informatica e Telecomunicazioni, Azienda Sanitaria Universitaria Giuliana Isontina, 34149 Trieste, Italy; graziano.depetris@asugi.sanita.fvg.it 4 Department of Medical, Surgical and Health Sciences, University of Trieste, Cattinara Academic Hospital, 34149 Trieste, Italy; deborah.bonazza@asugi.sanita.fvg.it (D.B.) * Correspondence: ll@terraquantum.swiss (L.L.); fabio.cavalli@asugi.sanita.fvg.it (F.C.) Abstract: 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. Keywords: medical image classification; hybrid quantum ResNet; quantum machine learning; hybrid quantum neural network; federated learning; computer-aided diagnosis; histology 1. Introduction 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 re- quirements 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. Diagnostics 2024, 14, 558. https://doi.org/10.3390/diagnostics14050558 https://www.mdpi.com/journal/diagnostics