{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:50:09Z","timestamp":1767315009636,"version":"3.48.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032101914","type":"print"},{"value":"9783032101921","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-10192-1_40","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:47:15Z","timestamp":1767314835000},"page":"475-486","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A SAM-Based Automated Schistocyte Detection Pipeline in\u00a0Peripheral Blood Smear Images"],"prefix":"10.1007","author":[{"given":"Ahmed","family":"Bensaid","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5361-8793","authenticated-orcid":false,"given":"Lorenzo","family":"Putzu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6571-3816","authenticated-orcid":false,"family":"Andrea Loddo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4641-0307","authenticated-orcid":false,"given":"Cecilia","family":"Di Ruberto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Bain, B.J.: Blood Cells: A Practical Guide. Wiley (2021)","DOI":"10.1002\/9781119820307"},{"key":"40_CR2","doi-asserted-by":"publisher","unstructured":"Christensen, R.D.: Chapter 43-neonatal anemia. In: Maheshwari, A. (ed.), Principles of Neonatology, pp. 357\u2013379. Elsevier, New Delhi (2024). https:\/\/doi.org\/10.1016\/B978-0-323-69415-5.00043-6, https:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780323694155000436","DOI":"10.1016\/B978-0-323-69415-5.00043-6"},{"key":"40_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105298","volume":"151","author":"A Genovese","year":"2024","unstructured":"Genovese, A., Piuri, V., Scotti, F.: A decision support system for acute lymphoblastic leukemia detection based on explainable artificial intelligence. Image Vis. Comput. 151, 105298 (2024)","journal-title":"Image Vis. Comput."},{"key":"40_CR4","doi-asserted-by":"publisher","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2961\u20132969 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.322, https:\/\/openaccess.thecvf.com\/content_iccv_2017\/html\/He_Mask_R-CNN_ICCV_2017_paper.html","DOI":"10.1109\/ICCV.2017.322"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Doll\u00e1r, P., Girshick, R.: Segment Anything (2023). https:\/\/arxiv.org\/abs\/2304.02643","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"40_CR6","doi-asserted-by":"publisher","unstructured":"Koch, V., Wagner, S.J., Kazeminia, S., Sancar, E., Hehr, M., Schnabel, J.A., Peng, T., Marr, C.: Dinobloom: A foundation model for generalizable cell embeddings in hematology. In: Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2024\u201427th International Conference, Marrakesh, Morocco, 6\u201310 Oct. 2024, Proceedings, Part XII. Lecture Notes in Computer Science, vol. 15012, pp. 520\u2013530. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-72390-2_49, https:\/\/doi.org\/10.1007\/978-3-031-72390-2_49","DOI":"10.1007\/978-3-031-72390-2_49"},{"key":"40_CR7","unstructured":"Leeds, U.: The Histology Guide (2021). Accessed 23 June 2023 https:\/\/www.histology.leeds.ac.uk\/blood\/blood_wbc.php"},{"issue":"1","key":"40_CR8","doi-asserted-by":"publisher","first-page":"2160","DOI":"10.1038\/s41598-023-29331-3","volume":"13","author":"M Li","year":"2023","unstructured":"Li, M., Lin, C., Ge, P., Li, L., Song, S., Zhang, H., Lu, L., Liu, X., Zheng, F., Zhang, S., et al.: A deep learning model for detection of leukocytes under various interference factors. Sci. Rep. 13(1), 2160 (2023)","journal-title":"Sci. Rep."},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Loddo, A., Putzu, L.: On the effectiveness of leukocytes classification methods in a real application scenario. Ai 2(3), 394\u2013412 (2021)","DOI":"10.3390\/ai2030025"},{"key":"40_CR10","doi-asserted-by":"publisher","unstructured":"Loddo, A., Putzu, L.: On the effectiveness of leukocytes classification methods in a real application scenario. AI 2(3), 394\u2013412 (2021). https:\/\/doi.org\/10.3390\/ai2030025, https:\/\/www.mdpi.com\/2673-2688\/2\/3\/25","DOI":"10.3390\/ai2030025"},{"key":"40_CR11","doi-asserted-by":"publisher","unstructured":"Loddo, A., Putzu, L.: On the reliability of cnns in clinical practice: a computer-aided diagnosis system case study. Appl. Sci. 12(7) (2022). https:\/\/doi.org\/10.3390\/app12073269, https:\/\/www.mdpi.com\/2076-3417\/12\/7\/3269","DOI":"10.3390\/app12073269"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Madni, H.A., Umer, R.M., Zottin, S., Marr, C., Foresti, G.L.: FL-W3S: cross-domain federated learning for weakly supervised semantic segmentation of white blood cells. Int. J. Med. Inf. 195, 105806 (2025). 10.1016\/J.IJMEDINF.2025.105806, https:\/\/doi.org\/10.1016\/j.ijmedinf.2025.105806","DOI":"10.1016\/j.ijmedinf.2025.105806"},{"key":"40_CR13","unstructured":"Mura, D.A., Pinna, M., Putzu, L., Loddo, A., Perniciano, A., Mulas, O., Di Ruberto, C.: Exploring few-shot object detection on blood smear images: a case study of leukocytes and schistocytes. In: Proceedings of ITADATA2024: The 3rd Italian Conference on Big Data and Data Science. ITADATA, Italy (2024)"},{"key":"40_CR14","doi-asserted-by":"publisher","unstructured":"Mura, D.A., Zedda, L., Loddo, A., Di\u00a0Ruberto, C.: Yolo-tryppa: a novel yolo-based approach for rapid and accurate detection of small trypanosoma parasites. J. Imaging 11(4) (2025). https:\/\/doi.org\/10.3390\/jimaging11040117, https:\/\/www.mdpi.com\/2313-433X\/11\/4\/117","DOI":"10.3390\/jimaging11040117"},{"key":"40_CR15","doi-asserted-by":"publisher","unstructured":"Pachetti, E., Colantonio, S.: A systematic review of few-shot learning in medical imaging. Artif. Intell. Med. 156, 102949 (2024) .https:\/\/doi.org\/10.1016\/J.ARTMED.2024.102949, https:\/\/doi.org\/10.1016\/j.artmed.2024.102949","DOI":"10.1016\/J.ARTMED.2024.102949"},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"Putzu, L., Loddo, A., Porcu, S.: White blood cell classification via distributed collaborative machine learning. SSRN 4732432 (2024)","DOI":"10.2139\/ssrn.4732432"},{"key":"40_CR17","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 91\u201399 (2015)"},{"key":"40_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106028","volume":"150","author":"S Saleem","year":"2022","unstructured":"Saleem, S., Amin, J., Sharif, M., Mallah, G.A., Kadry, S., Gandomi, A.H.: Leukemia segmentation and classification: a comprehensive survey. Comput. Biol. Med. 150, 106028 (2022)","journal-title":"Comput. Biol. Med."},{"key":"40_CR19","doi-asserted-by":"publisher","unstructured":"Zedda, L., Loddo, A., Ruberto, C.D.: A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites. Biomed. Signal Process. Control 94, 106289 (2024). https:\/\/doi.org\/10.1016\/J.BSPC.2024.106289, https:\/\/doi.org\/10.1016\/j.bspc.2024.106289","DOI":"10.1016\/J.BSPC.2024.106289"},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Zedda, L., Loddo, A., Ruberto, C.D.: A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites in a realistic scenario. Comput. Biol. Medicine 186, 109704 (2025). 10.1016\/J.COMPBIOMED.2025.109704, https:\/\/doi.org\/10.1016\/j.compbiomed.2025.109704","DOI":"10.1016\/j.compbiomed.2025.109704"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-10192-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:47:16Z","timestamp":1767314836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-10192-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032101914","9783032101921"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-10192-1_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap.org\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}