{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:59:31Z","timestamp":1785340771675,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Institutes of Health (NIH)","award":["R01CA297855"],"award-info":[{"award-number":["R01CA297855"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,30]]},"DOI":"10.1145\/3807503.3819368","type":"proceedings-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:55:27Z","timestamp":1785293727000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum Information-Inspired Distance Functions for Siamese Networks in Longitudinal Mammogram Imaging"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2015-9820","authenticated-orcid":false,"given":"Sahand","family":"Hamzehei","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, University of Connecticut, Storrs, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1083-2138","authenticated-orcid":false,"given":"Mostafa","family":"Karami","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of Connecticut, Storrs, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-9600","authenticated-orcid":false,"given":"Afsana","family":"Ahsan Jeny","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of Connecticut, Storrs, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8866-2999","authenticated-orcid":false,"given":"Stephen","family":"Andrew Baker","sequence":"additional","affiliation":[{"name":"Radiology, University of Connecticut Health, Farmington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3986-1582","authenticated-orcid":false,"given":"Tucker","family":"Van Rathe","sequence":"additional","affiliation":[{"name":"Radiology, University of Connecticut Health, Farmington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7108-2021","authenticated-orcid":false,"given":"Clifford","family":"Yang","sequence":"additional","affiliation":[{"name":"Radiology, University of Connecticut Health, Farmington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5996-1020","authenticated-orcid":false,"given":"Sheida","family":"Nabavi","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of Connecticut, Storrs, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Jun Bai Annie Jin Tianyu Wang Clifford Yang and Sheida Nabavi. 2022. Feature fusion Siamese network for breast cancer detection comparing current and prior mammograms. Medical Physics 49 6 (2022) 3654\u20133669.","DOI":"10.1002\/mp.15598"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Mateusz Buda Ashirbani Saha Ruth Walsh Sujata Ghate Nianyi Li Albert \u015awi\u0119cicki Joseph\u00a0Y Lo and Maciej\u00a0A Mazurowski. 2021. A data set and deep learning algorithm for the detection of masses and architectural distortions in digital breast tomosynthesis images. JAMA network open 4 8 (2021) e2119100\u2013e2119100.","DOI":"10.1001\/jamanetworkopen.2021.19100"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Ronald\u00a0A Castellino. 2005. Computer aided detection (CAD): an overview. Cancer Imaging 5 1 (2005) 17.","DOI":"10.1102\/1470-7330.2005.0018"},{"key":"e_1_3_3_2_5_2","unstructured":"Chunyan Cui Li Li Hongmin Cai Zhihao Fan Ling Zhang Tingting Dan Jiao Li and Jinghua Wang. 2021. The Chinese Mammography Database (CMMD): An online mammography database with biopsy confirmed types for machine diagnosis of breast. The Cancer Imaging Archive 1 (2021)."},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM62325.2024.10822291"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Afsana\u00a0Ahsan Jeny Sahand Hamzehei Annie Jin Stephen\u00a0Andrew Baker Tucker Van\u00a0Rathe Jun Bai Clifford Yang and Sheida Nabavi. 2025. Hybrid transformer-based model for mammogram classification by integrating prior and current images. Medical Physics 52 5 (2025) 2999\u20133014.","DOI":"10.1002\/mp.17650"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Jiwoong\u00a0J Jeong Brianna\u00a0L Vey Ananth Bhimireddy Thomas Kim Thiago Santos Ramon Correa Raman Dutt Marina Mosunjac Gabriela Oprea-Ilies Geoffrey Smith et\u00a0al. 2023. The EMory BrEast imaging Dataset (EMBED): A racially diverse granular dataset of 3.4 million screening and diagnostic mammographic images. Radiology: Artificial Intelligence 5 1 (2023) e220047.","DOI":"10.1148\/ryai.220047"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Joanne Kim Andrew Harper Valerie McCormack Hyuna Sung Nehmat Houssami Eileen Morgan Miriam Mutebi Gail Garvey Isabelle Soerjomataram and Miranda\u00a0M Fidler-Benaoudia. 2025. Global patterns and trends in breast cancer incidence and mortality across 185 countries. Nature medicine 31 4 (2025) 1154\u20131162.","DOI":"10.1038\/s41591-025-03502-3"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Zan Klanecek Yao-Kuan Wang Tobias Wagner Lesley Cockmartin Nicholas Marshall Brayden Schott Ali Deatsch Andrej Studen Katja Jarm Mateja Krajc et\u00a0al. 2025. Longitudinal interpretability of deep learning based breast cancer risk prediction. Physics in Medicine & Biology 70 1 (2025) 015001.","DOI":"10.1088\/1361-6560\/ad9db3"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43904-9_38"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91755-9"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Rebecca\u00a0Sawyer Lee Francisco Gimenez Assaf Hoogi Kanae\u00a0Kawai Miyake Mia Gorovoy and Daniel\u00a0L Rubin. 2017. A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific data 4 1 (2017) 1\u20139.","DOI":"10.1038\/sdata.2017.177"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Yeong-Cherng Liang Yu-Hao Yeh Paulo\u00a0EMF Mendon\u00e7a Run\u00a0Yan Teh Margaret\u00a0D Reid and Peter\u00a0D Drummond. 2019. Quantum fidelity measures for mixed states. Reports on Progress in Physics 82 7 (2019) 076001.","DOI":"10.1088\/1361-6633\/ab1ca4"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9433802"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","unstructured":"Kosmia Loizidou Galateia Skouroumouni Gabriella Savvidou Anastasia Constantinidou Eleni\u00a0Orphanidou Vlachou Anneza Yiallourou Costas Pitris and Christos Nikolaou. 2024. Breast Masses Dataset with Precisely Annotated Sequential Mammograms. 10.5281\/zenodo.7179855You can cite all versions by using the DOI 10.5281\/zenodo.7179855. This DOI represents all versions and will always resolve to the latest one..","DOI":"10.5281\/zenodo.7179855"},{"key":"e_1_3_3_2_17_2","unstructured":"Prasanta\u00a0Chandra Mahalanobis. 2018. On the generalized distance in statistics. Sankhy\u0101: The Indian Journal of Statistics Series A (2008-) 80 (2018) S1\u2013S7."},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Sebastien\u00a0Jean Mambou Petra Maresova Ondrej Krejcar Ali Selamat and Kamil Kuca. 2018. Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors 18 9 (2018) 2799.","DOI":"10.3390\/s18092799"},{"key":"e_1_3_3_2_19_2","unstructured":"B Mat\u00e9rn. 1960. Spatial variation: Meddelanden fran statens skogsforskningsinstitut. Lecture Notes in Statistics 36 (1960) 21."},{"key":"e_1_3_3_2_20_2","unstructured":"Jungkyu Park Jason Phang Yiqiu Shen Nan Wu S Kim Linda Moy Kyunghyun Cho and Krzysztof\u00a0J Geras. 2019. Screening mammogram classification with prior exams. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1907.13057 (2019)."},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Bayan Sardini Mette\u00a0Bach Larsen and Sisse\u00a0Helle Njor. 2025. Do breast cancer survivors benefit from mammography screening? A population-based study. Cancer Epidemiology 99 (2025) 102910.","DOI":"10.1016\/j.canep.2025.102910"},{"key":"e_1_3_3_2_22_2","first-page":"71","volume-title":"International Conference on Software Engineering and Formal Methods","author":"Schlagenhauf Tobias","year":"2022","unstructured":"Tobias Schlagenhauf, Faruk Yildirim, and Benedikt Br\u00fcckner. 2022. Siamese basis function networks for data-efficient defect classification in technical domains. In International Conference on Software Engineering and Formal Methods. Springer, 71\u201392."},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Matthias Seeger. 2004. Gaussian processes for machine learning. International journal of neural systems 14 02 (2004) 69\u2013106.","DOI":"10.1142\/S0129065704001899"},{"key":"e_1_3_3_2_24_2","volume-title":"Learning with kernels","author":"Smola Alexander\u00a0J","year":"1998","unstructured":"Alexander\u00a0J Smola and Bernhard Sch\u00f6lkopf. 1998. Learning with kernels. Vol.\u00a04. GMD-Forschungszentrum Informationstechnik Berlin, Germany."}],"event":{"name":"BCB '26: 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","location":"Rende (CS) Italy","acronym":"BCB '26","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3807503.3819368","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:14:40Z","timestamp":1785338080000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3807503.3819368"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":23,"alternative-id":["10.1145\/3807503.3819368","10.1145\/3807503"],"URL":"https:\/\/doi.org\/10.1145\/3807503.3819368","relation":{},"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"2026-07-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}