Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification

arXiv:2405.00156v1 Announce Type: cross
Abstract: Quantum machine learning (QML) has the potential for improving the multi-label classification of rare, albeit critical, diseases in large-scale chest x-ray (CXR) datasets due to theoretical quantum advantages over classical machine learning (CML) in sample efficiency and generalizability. While prior literature has explored QML with CXRs, it has focused on binary classification tasks with small datasets due to limited access to quantum hardware and computationally expensive simulations. To that end, we implemented a Jax-based framework …

abstract advantages arxiv classification cs.ai cs.cv cs.lg datasets diseases efficiency enabling horizon hybrid improving literature machine machine learning prior qml quant-ph quantum ray sample scale transfer transfer learning type while x-ray