{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T05:28:27Z","timestamp":1767245307872,"version":"3.48.0"},"reference-count":46,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["RGPIN-2024-05287"],"award-info":[{"award-number":["RGPIN-2024-05287"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"name":"AI in Health Research Chair at the Universit\u00e9 de Moncton and the Mitacs Globalink Research Award","award":["IT39642"],"award-info":[{"award-number":["IT39642"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Background: Quantum Machine Learning (QML) has attracted significant attention in recent years. With quantum computing achievements in computationally costly domains, discovering its potential in improving the performance and efficiency of deep learning models in medical imaging has become a promising field of research. Methods: We investigate QML in healthcare by developing a novel quantum-enhanced U-Net (QU-Net). We experiment with six configurations of parameterized quantum circuits, varying the encoding technique (amplitude vs. angle), depth and entanglement. Using the ISIC-2017 skin cancer dataset, we compare QU-Net with classical U-Net on self-supervised image reconstruction and binary classification of benign and malignant skin cancer, where we combine bottleneck embeddings with patient metadata. Results: Our findings show that amplitude encoding stabilizes training, whereas angle encoding introduces fluctuations. The best performance is obtained with amplitude encoding and one layer. For reconstruction, QU-Net with entanglement converges faster (25 epochs vs. 44) with a lower Mean Squared Error per image (0.00015 vs. 0.00017) on unseen data. For classification, QU-Net with no entanglement embeddings reaches 79.03% F1-score compared with 74.14% for U-Net, despite compressing images to a smaller latent space (7 vs. 128). Conclusions: These results demonstrate that the quantum layer enhances U-Net\u2019s expressive power with efficient data embedding.<\/jats:p>","DOI":"10.3390\/bdcc10010012","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T13:30:52Z","timestamp":1767187852000},"page":"12","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["QU-Net: Quantum-Enhanced U-Net for Self Supervised Embedding and Classification of Skin Cancer Images"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1276-6376","authenticated-orcid":false,"given":"Khidhr","family":"Halab","sequence":"first","affiliation":[{"name":"Perception, Robotics, and Intelligent Machines (PRIME) Laboratory, Department of Computer Science, Universit\u00e9 de Moncton, Moncton, NB E1A 3E9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7053-5928","authenticated-orcid":false,"given":"Nabil","family":"Marzoug","sequence":"additional","affiliation":[{"name":"Perception, Robotics, and Intelligent Machines (PRIME) Laboratory, Department of Computer Science, Universit\u00e9 de Moncton, Moncton, NB E1A 3E9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2421-3959","authenticated-orcid":false,"given":"Othmane","family":"El Meslouhi","sequence":"additional","affiliation":[{"name":"LMPEQ Laboratory, National School of Applied Sciences, Cadi Ayyad University, Safi 46000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0051-9374","authenticated-orcid":false,"given":"Zouhair Elamrani","family":"Abou Elassad","sequence":"additional","affiliation":[{"name":"LISI Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech 40000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4378-2669","authenticated-orcid":false,"given":"Moulay A.","family":"Akhloufi","sequence":"additional","affiliation":[{"name":"Perception, Robotics, and Intelligent Machines (PRIME) Laboratory, Department of Computer Science, Universit\u00e9 de Moncton, Moncton, NB E1A 3E9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1038\/s43588-022-00311-3","article-title":"Challenges and opportunities in quantum machine learning","volume":"2","author":"Cerezo","year":"2022","journal-title":"Nat. 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