Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis

arXiv:2311.02402v2 Announce Type: replace
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 …

abstract arxiv cases classification cs.cv cs.lg diagnosis diagnostic easy expert federated learning focus hybrid image precision quant-ph quantum solve study type