{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T20:59:29Z","timestamp":1789678769344,"version":"4.0.1"},"reference-count":60,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T00:00:00Z","timestamp":1687651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Cardiovascular diseases are among the major health problems that are likely to benefit from promising developments in quantum machine learning for medical imaging. The chest X-ray (CXR), a widely used modality, can reveal cardiomegaly, even when performed primarily for a non-cardiological indication. Based on pre-trained DenseNet-121, we designed hybrid classical\u2013quantum (CQ) transfer learning models to detect cardiomegaly in CXRs. Using Qiskit and PennyLane, we integrated a parameterized quantum circuit into a classic network implemented in PyTorch. We mined the CheXpert public repository to create a balanced dataset with 2436 posteroanterior CXRs from different patients distributed between cardiomegaly and the control. Using k-fold cross-validation, the CQ models were trained using a state vector simulator. The normalized global effective dimension allowed us to compare the trainability in the CQ models run on Qiskit. For prediction, ROC AUC scores up to 0.93 and accuracies up to 0.87 were achieved for several CQ models, rivaling the classical\u2013classical (CC) model used as a reference. A trustworthy Grad-CAM++ heatmap with a hot zone covering the heart was visualized more often with the QC option than that with the CC option (94% vs. 61%, p &lt; 0.001), which may boost the rate of acceptance by health professionals.<\/jats:p>","DOI":"10.3390\/jimaging9070128","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T02:05:02Z","timestamp":1687745102000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Hybrid Classical\u2013Quantum Transfer Learning for Cardiomegaly Detection in Chest X-rays"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4223-6532","authenticated-orcid":false,"given":"Pierre","family":"Decoodt","sequence":"first","affiliation":[{"name":"Cardiologie, Centre Hospitalo-Universitaire Brugmann, Facult\u00e9 de M\u00e9decine, Universit\u00e9 Libre de Bruxelles, Place Van Gehuchten 4, 1020 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4476-966X","authenticated-orcid":false,"given":"Tan Jun","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Digital Health and Innovations Impact Lab, Taylor\u2019s University, Subang Jaya 47500, Selangor, Malaysia"},{"name":"qBraid Co., Chicago, IL 60615, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0453-8196","authenticated-orcid":false,"given":"Soham","family":"Bopardikar","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune 411005, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hemavathi","family":"Santhanam","sequence":"additional","affiliation":[{"name":"Faculty of Graduate Studies and Research, Saint Mary\u2019s University, 923 Robie Street, Halifax, NS B3H 3C3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9101-4314","authenticated-orcid":false,"given":"Alfaxad","family":"Eyembe","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Kyoto University of Advanced Science (KUAS), Ukyo-ku, Kyoto 615-8577, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9356-1186","authenticated-orcid":false,"given":"Begonya","family":"Garcia-Zapirain","sequence":"additional","affiliation":[{"name":"eVIDA Research Group, Department of Engineering, Deusto University, 48007 Bilbao, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1326-0867","authenticated-orcid":false,"given":"Daniel","family":"Sierra-Sosa","sequence":"additional","affiliation":[{"name":"Computer Science and Information Technologies Department, Hood College, 401 Rosemont Ave., Frederick, MD 21702, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,25]]},"reference":[{"key":"ref_1","unstructured":"(2023, March 26). 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