{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T19:26:31Z","timestamp":1771529191996,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Health from Portugal - Agenda Mobilizadora para a Inova\u00e7\u00e3o Empresarial","award":["C644937233-00000047"],"award-info":[{"award-number":["C644937233-00000047"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Automated image analysis, supported by powerful artificial intelligence algorithms, promises significant workflow advantages in the screening of medical images. The ability to automatically detect and classify objects of interest can drastically reduce the screening time, reduce observer variability, and help doctors in the diagnosis and report formulation. These advancements have gathered interest from the medical community, and several imaging platforms have evolved and adapted to this new reality, developing new analysis algorithms or providing interfaces to develop and integrate new ones. These applications have grown and thrived in a non-standardized environment, which means reutilizing these algorithms in different applications or sharing the results they output is not always possible. Additionally, developing these algorithms takes time and needs labeled datasets, which are not easily acquired. These factors limit the reach and applicability of these algorithms. This paper presents a framework that intends to address the standardization issue in medical image analysis by facilitating the integration and development of new algorithms in a production-ready imaging archive. The work proposes a new open-source interface, based on standard industry protocols, to be integrated into the open-source vendor-neutral PACS Dicoogle.<\/jats:p>","DOI":"10.1007\/s10278-026-01856-9","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:55:53Z","timestamp":1771527353000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Plugin-Based Architecture for Integrating AI Services in an Open-Source PACS"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9231-6744","authenticated-orcid":false,"given":"Rui","family":"Jesus","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8513-7185","authenticated-orcid":false,"given":"Lu\u00eds Basti\u00e3o","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4371-8632","authenticated-orcid":false,"given":"Marcos Gestal","family":"Pose","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2707-5331","authenticated-orcid":false,"given":"Carlos","family":"Costa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"1856_CR1","doi-asserted-by":"publisher","unstructured":"A.\u00a0Barrag\u00e1n-Montero, U.\u00a0Javaid, G.\u00a0Vald\u00e9s, D.\u00a0Nguyen, P.\u00a0Desbordes, B.\u00a0Macq, S.\u00a0Willems, L.\u00a0Vandewinckele, M.\u00a0Holmstr\u00f6m, F.\u00a0L\u00f6fman, S.\u00a0Michiels, K.\u00a0Souris, E.\u00a0Sterpin, J.\u00a0A. Lee, Artificial intelligence and machine learning for medical imaging: A technology review, Physica Medica 83 (2021) 242\u2013256. https:\/\/doi.org\/10.1016\/J.EJMP.2021.04.016.","DOI":"10.1016\/J.EJMP.2021.04.016"},{"key":"1856_CR2","doi-asserted-by":"publisher","unstructured":"S.\u00a0Robertson, H.\u00a0Azizpour, K.\u00a0Smith, J.\u00a0Hartman, Digital image analysis in breast pathology\u2014from image processing techniques to artificial intelligence, Translational Research 194 (2018) 19\u201335. https:\/\/doi.org\/10.1016\/j.trsl.2017.10.010","DOI":"10.1016\/j.trsl.2017.10.010"},{"key":"1856_CR3","doi-asserted-by":"publisher","unstructured":"E.\u00a0Patterson, M.\u00a0Rayo, C.\u00a0Gill, M.\u00a0Gurcan, Barriers and facilitators to adoption of soft copy interpretation from the user perspective: Lessons learned from filmless radiology for slideless pathology, Journal of Pathology Informatics 2\u00a0(1) (2011) 1. https:\/\/doi.org\/10.4103\/2153-3539.74940.","DOI":"10.4103\/2153-3539.74940"},{"key":"1856_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105596","volume":"194","author":"S Sengupta","year":"2020","unstructured":"S.\u00a0Sengupta, S.\u00a0Basak, P.\u00a0Saikia, S.\u00a0Paul, V.\u00a0Tsalavoutis, F.\u00a0Atiah, V.\u00a0Ravi, A.\u00a0Peters, A review of deep learning with special emphasis on architectures, applications and recent trends, Knowledge-Based Systems 194 (2020) 105596.","journal-title":"Knowledge-Based Systems"},{"key":"1856_CR5","doi-asserted-by":"publisher","unstructured":"S.\u00a0S. Mehrnia, Z.\u00a0Safahi, A.\u00a0Mousavi, \u00b7.\u00a0Fatemeh Panahandeh, A.\u00a0Farmani, \u00b7.\u00a0Ren Yuan, A.\u00a0Rahmim, M.\u00a0R. Salmanpour, Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review, Journal of Imaging Informatics in Medicine 2025 (2025) 1\u201330 https:\/\/doi.org\/10.1007\/S10278-025-01458-X","DOI":"10.1007\/S10278-025-01458-X"},{"key":"1856_CR6","doi-asserted-by":"publisher","unstructured":"A.\u00a0Fiorin, \u00b7.\u00a0Carlos L\u00f3pez Pablo, M.\u00a0Lejeune, \u00b7.\u00a0Ameer, H.\u00a0Siraj, \u00b7.\u00a0Vincenzo, D.\u00a0Mea, Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets, Journal of Imaging Informatics in Medicine 2024 37:6 37\u00a0(6) (2024) 2996\u20133008. https:\/\/doi.org\/10.1007\/S10278-024-01043-8","DOI":"10.1007\/S10278-024-01043-8"},{"key":"1856_CR7","doi-asserted-by":"publisher","unstructured":"K.\u00a0Magudia, C.\u00a0P. Bridge, K.\u00a0P. Andriole, M.\u00a0H. Rosenthal, The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications, Journal of Digital Imaging 34\u00a0(6) (2021) 1424\u20131429. https:\/\/doi.org\/10.1007\/S10278-021-00505-7\/TABLES\/1.","DOI":"10.1007\/S10278-021-00505-7\/TABLES\/1"},{"key":"1856_CR8","doi-asserted-by":"publisher","unstructured":"S.\u00a0K.\u00a0K. Santu, M.\u00a0M. Hassan, M.\u00a0J. Smith, L.\u00a0Xu, C.\u00a0Zhai, K.\u00a0Veeramachaneni, Automl to date and beyond: Challenges and opportunities, ACM Computing Surveys 54 (10 2020). https:\/\/doi.org\/10.1145\/3470918","DOI":"10.1145\/3470918"},{"key":"1856_CR9","doi-asserted-by":"publisher","unstructured":"X.\u00a0He, K.\u00a0Zhao, X.\u00a0Chu, AutoML: A survey of the state-of-the-art, Knowledge-Based Systems 212 (2021) 106622. arXiv:1908.00709https:\/\/doi.org\/10.1016\/J.KNOSYS.2020.106622","DOI":"10.1016\/J.KNOSYS.2020.106622"},{"key":"1856_CR10","doi-asserted-by":"publisher","unstructured":"M.\u00a0Salvi, U.\u00a0R. Acharya, F.\u00a0Molinari, K.\u00a0M. Meiburger, The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis, Computers in Biology and Medicine 128 (2021) 104129. https:\/\/doi.org\/10.1016\/J.COMPBIOMED.2020.104129","DOI":"10.1016\/J.COMPBIOMED.2020.104129"},{"key":"1856_CR11","doi-asserted-by":"publisher","unstructured":"K.\u00a0K. Wong, M.\u00a0Ayoub, Z.\u00a0Cao, C.\u00a0Chen, W.\u00a0Chen, D.\u00a0N. Ghista, C.\u00a0W. Zhang, The synergy of cybernetical intelligence with medical image analysis for deep medicine: A methodological perspective, Computer Methods and Programs in Biomedicine 240 (2023) 107677. https:\/\/doi.org\/10.1016\/J.CMPB.2023.107677. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260723003425","DOI":"10.1016\/J.CMPB.2023.107677"},{"key":"1856_CR12","doi-asserted-by":"publisher","unstructured":"S.\u00a0Vahdati, B.\u00a0Khosravi, E.\u00a0Mahmoudi, \u00b7.\u00a0Kuan Zhang, P.\u00a0Rouzrokh, S.\u00a0Faghani, M.\u00a0Moassefi, A.\u00a0Tahmasebi, K.\u00a0P. Andriole, \u00b7.\u00a0P. Chang, K.\u00a0Farahani, M.\u00a0G. Flores, L.\u00a0Folio, S.\u00a0Houshmand, M.\u00a0L. Giger, J.\u00a0W. Gichoya, \u00b7.\u00a0Bradley, J.\u00a0Erickson, A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey, Journal of Imaging Informatics in Medicine 2024 37:5 37\u00a0(5) (2024) 2015\u20132024. https:\/\/doi.org\/10.1007\/S10278-024-01083-0. https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01083-0","DOI":"10.1007\/S10278-024-01083-0"},{"key":"1856_CR13","doi-asserted-by":"publisher","unstructured":"D.\u00a0Montezuma, S.\u00a0P. Oliveira, Y.\u00a0Tolkach, P.\u00a0Boor, A.\u00a0Haragan, R.\u00a0Carvalho, V.\u00a0Della Mea, T.\u00a0R. Kiehl, S.\u00a0Leh, M.\u00a0Yousif, D.\u00a0Ameisen, Mircea-Sebastian \u0218erb\u0103nescu, N.\u00a0Zerbe, V.\u00a0L\u2019Imperio, Annotation Practices in Computational Pathology: A European Society of Digital and Integrative Pathology (ESDIP) Survey Study, Laboratory Investigation 105\u00a0(3) (2025) 102203. https:\/\/doi.org\/10.1016\/J.LABINV.2024.102203.","DOI":"10.1016\/J.LABINV.2024.102203"},{"key":"1856_CR14","doi-asserted-by":"publisher","unstructured":"L.\u00a0Faes, S.\u00a0K. Wagner, D.\u00a0J. Fu, X.\u00a0Liu, E.\u00a0Korot, J.\u00a0R. Ledsam, T.\u00a0Back, R.\u00a0Chopra, N.\u00a0Pontikos, C.\u00a0Kern, G.\u00a0Moraes, M.\u00a0K. Schmid, D.\u00a0Sim, K.\u00a0Balaskas, L.\u00a0M. Bachmann, A.\u00a0K. Denniston, P.\u00a0A. Keane, Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study, The Lancet Digital Health 1 (2019) e232\u2013e242. https:\/\/doi.org\/10.1016\/S2589-7500(19)30108-6. http:\/\/www.thelancet.com\/article\/S2589750019301086\/fulltext","DOI":"10.1016\/S2589-7500(19)30108-6"},{"key":"1856_CR15","doi-asserted-by":"publisher","unstructured":"M.\u00a0Alhajeri, S.\u00a0G.\u00a0S. Shah, Limitations in and solutions for improving the functionality of picture archiving and communication system: an exploratory study of pacs professionals\u2019 perspectives, Journal of Digital Imaging 32 (2019) 54\u201367. https:\/\/doi.org\/10.1007\/S10278-018-0127-2\/METRICS. https:\/\/link.springer.com\/article\/10.1007\/s10278-018-0127-2","DOI":"10.1007\/S10278-018-0127-2\/METRICS"},{"key":"1856_CR16","doi-asserted-by":"publisher","unstructured":"D.\u00a0A. Clunie, Dicom format and protocol standardization\u2014a core requirement for digital pathology success:, https:\/\/doi.org\/10.1177\/0192623320965893 49 (2020) 738\u2013749. https:\/\/doi.org\/10.1177\/0192623320965893. https:\/\/journals.sagepub.com\/doi\/abs\/10.1177\/0192623320965893","DOI":"10.1177\/0192623320965893"},{"key":"1856_CR17","unstructured":"NEMA, Digital imaging and communications in medicine (dicom) part 5: Data structures and encoding (2023). https:\/\/dicom.nema.org\/medical\/dicom\/current\/output\/pdf\/part05.pdf"},{"key":"1856_CR18","unstructured":"D.\u00a0A. Clunie, Dicom structured reporting (2000)."},{"key":"1856_CR19","unstructured":"D.\u00a0S. Committee, Digital imaging and communications in medicine(dicom) supplement 172: Parametric map storage (2014). https:\/\/www.dicomstandard.org\/News-dir\/ftsup\/docs\/sups\/sup172.pdf"},{"key":"1856_CR20","unstructured":"D.\u00a0S. Committee, Digital imaging and communications in medicine (dicom) supplement 111: Segmentation storage sop class (2006). https:\/\/www.dicomstandard.org\/News-dir\/ftsup\/docs\/sups\/sup111.pdf"},{"key":"1856_CR21","unstructured":"D.\u00a0S. Committee, Digital imaging and communications in medicine (dicom) supplement 222: Microscopy bulk simple annotations storage sop class (2021). https:\/\/www.dicomstandard.org\/News-dir\/ftsup\/docs\/sups\/sup222.pdf"},{"key":"1856_CR22","unstructured":"D.\u00a0Commitee,Wg-23: Artificial intelligence\/application hosting. https:\/\/www.dicomstandard.org\/activity\/wgs\/wg-23"},{"key":"1856_CR23","unstructured":"D.\u00a0Commitee,Digital imaging and communications in medicine (dicom) supplement 219 - json representation of dicom structured reports (2017). https:\/\/www.dicomstandard.org\/News-dir\/ftsup\/docs\/sups\/Sup219.pdf"},{"key":"1856_CR24","doi-asserted-by":"publisher","unstructured":"R.\u00a0Lebre, E.\u00a0Pinho, R.\u00a0Jesus, L.\u00a0Basti\u00e3o, C.\u00a0Costa, Correction to: Dicoogle open source: The establishment of a new paradigm in medical imaging (journal of medical systems, (2022), 46, 11, (77), 10.1007\/s10916-022-01867-3), Journal of Medical Systems 46 (2022) 1\u20131. https:\/\/doi.org\/10.1007\/S10916-022-01882-4\/METRICS. https:\/\/link.springer.com\/article\/10.1007\/s10916-022-01882-4","DOI":"10.1007\/S10916-022-01882-4\/METRICS"},{"key":"1856_CR25","doi-asserted-by":"publisher","unstructured":"M.\u00a0Pachitariu, C.\u00a0Stringer, Cellpose 2.0: how to train your own model, Nature Methods 2022 19:12 19 (2022) 1634\u20131641. https:\/\/doi.org\/10.1038\/s41592-022-01663-4. https:\/\/www.nature.com\/articles\/s41592-022-01663-4","DOI":"10.1038\/s41592-022-01663-4"},{"key":"1856_CR26","doi-asserted-by":"publisher","unstructured":"C.\u00a0T. Rueden, J.\u00a0Schindelin, M.\u00a0C. Hiner, B.\u00a0E. DeZonia, A.\u00a0E. Walter, E.\u00a0T. Arena, K.\u00a0W. Eliceiri, Imagej2: Imagej for the next generation of scientific image data, BMC Bioinformatics 18 (2017) 1\u201326. https:\/\/doi.org\/10.1186\/S12859-017-1934-Z\/FIGURES\/7. https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-017-1934-z","DOI":"10.1186\/S12859-017-1934-Z\/FIGURES\/7"},{"key":"1856_CR27","doi-asserted-by":"publisher","unstructured":"D.\u00a0R. Stirling, M.\u00a0J. Swain-Bowden, A.\u00a0M. Lucas, A.\u00a0E. Carpenter, B.\u00a0A. Cimini, A.\u00a0Goodman, Cellprofiler 4: improvements in speed, utility and usability, BMC Bioinformatics 22 (2021) 1\u201311. https:\/\/doi.org\/10.1186\/S12859-021-04344-9\/FIGURES\/6. https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04344-9","DOI":"10.1186\/S12859-021-04344-9\/FIGURES\/6"},{"key":"1856_CR28","doi-asserted-by":"publisher","unstructured":"M.\u00a0P. Humphries, P.\u00a0Maxwell, M.\u00a0Salto-Tellez, Qupath: The global impact of an open source digital pathology system, Computational and Structural Biotechnology Journal 19 (2021) 852\u2013859. https:\/\/doi.org\/10.1016\/J.CSBJ.2021.01.022","DOI":"10.1016\/J.CSBJ.2021.01.022"},{"key":"1856_CR29","doi-asserted-by":"publisher","unstructured":"P.\u00a0D. L\u00f6sel, T.\u00a0van\u00a0de Kamp, A.\u00a0Jayme, A.\u00a0Ershov, T.\u00a0Farag\u00f3, O.\u00a0Pichler, N.\u00a0Tan Jerome, N.\u00a0Aadepu, S.\u00a0Bremer, S.\u00a0A. Chilingaryan, M.\u00a0Heethoff, A.\u00a0Kopmann, J.\u00a0Odar, S.\u00a0Schmelzle, M.\u00a0Zuber, J.\u00a0Wittbrodt, T.\u00a0Baumbach, V.\u00a0Heuveline, Introducing Biomedisa as an open-source online platform for biomedical image segmentation, Nature Communications 2020 11:1 11\u00a0(1) (2020) 1\u201314. https:\/\/doi.org\/10.1038\/s41467-020-19303-w. https:\/\/www.nature.com\/articles\/s41467-020-19303-w","DOI":"10.1038\/s41467-020-19303-w"},{"key":"1856_CR30","unstructured":"A.\u00a0Diaz-Pinto, S.\u00a0Alle, A.\u00a0Ihsani, M.\u00a0Asad, V.\u00a0Nath, F.\u00a0P\u00e9rez-Garc\u00eda, P.\u00a0Mehta, W.\u00a0Li, H.\u00a0R. Roth, T.\u00a0Vercauteren, D.\u00a0Xu, P.\u00a0Dogra, S.\u00a0Ourselin, A.\u00a0Feng, M.\u00a0J. Cardoso, MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images, arXiv e-prints (2022)."},{"key":"1856_CR31","doi-asserted-by":"publisher","unstructured":"J.\u00a0Ferreira, C.\u00a0Costa, Web platform for medical deep learning services (2022) 1727\u20131732 https:\/\/doi.org\/10.1109\/BIBM52615.2021.9669704.","DOI":"10.1109\/BIBM52615.2021.9669704"},{"key":"1856_CR32","doi-asserted-by":"publisher","unstructured":"A.\u00a0Fedorov, R.\u00a0Beichel, J.\u00a0Kalpathy-Cramer, J.\u00a0Finet, J.\u00a0C. Fillion-Robin, S.\u00a0Pujol, C.\u00a0Bauer, D.\u00a0Jennings, F.\u00a0Fennessy, M.\u00a0Sonka, J.\u00a0Buatti, S.\u00a0Aylward, J.\u00a0V. Miller, S.\u00a0Pieper, R.\u00a0Kikinis, 3d slicer as an image computing platform for the quantitative imaging network, Magnetic resonance imaging 30 (2012) 1323\u20131341. https:\/\/doi.org\/10.1016\/J.MRI.2012.05.001. https:\/\/pubmed.ncbi.nlm.nih.gov\/22770690\/","DOI":"10.1016\/J.MRI.2012.05.001"},{"key":"1856_CR33","doi-asserted-by":"publisher","unstructured":"al\u00a0Josh\u00a0Moore, M.\u00a0Linkert, C.\u00a0Blackburn, M.\u00a0Carroll, R.\u00a0K. Ferguson, H.\u00a0Flynn, K.\u00a0Gillen, R.\u00a0Leigh, S.\u00a0Li, D.\u00a0Lindner, W.\u00a0J. Moore, A.\u00a0J. Patterson, B.\u00a0Pindelski, B.\u00a0Ramalingam, E.\u00a0Rozbicki, A.\u00a0Tarkowska, P.\u00a0Walczysko, C.\u00a0Allan, J.-M. Burel, J.\u00a0Swedlow, J.\u00a0Moore, J.\u00a0R. Swedlow, Omero and bio-formats 5: flexible access to large bioimaging datasets at scale, 9413 (2015) 37\u201342. https:\/\/doi.org\/10.1117\/12.2086370. https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/9413\/941307\/OMERO-and-Bio-Formats-5--flexible-access-to-large\/10.1117\/12.2086370.full","DOI":"10.1117\/12.2086370"},{"key":"1856_CR34","doi-asserted-by":"publisher","unstructured":"S.\u00a0Jodogne, The orthanc ecosystem for medical imaging, Journal of Digital Imaging 31 (2018) 341\u2013352. https:\/\/doi.org\/10.1007\/S10278-018-0082-Y\/FIGURES\/14. https:\/\/link.springer.com\/article\/10.1007\/s10278-018-0082-y","DOI":"10.1007\/S10278-018-0082-Y\/FIGURES\/14"},{"key":"1856_CR35","doi-asserted-by":"publisher","unstructured":"X.\u00a0T. Li, J.\u00a0W. Allen, R.\u00a0Hu, Implementation of Automated Pipeline for Resting-State fMRI Analysis with PACS Integration, Journal of Digital Imaging 36\u00a0(3) (2023) 1189\u20131197. https:\/\/doi.org\/10.1007\/S10278-022-00758-W\/FIGURES\/6. https:\/\/link.springer.com\/article\/10.1007\/s10278-022-00758-w","DOI":"10.1007\/S10278-022-00758-W\/FIGURES\/6"},{"key":"1856_CR36","unstructured":"B.\u00a0Genereaux, K.\u00a0O\u2019donnell, B.\u00a0Bialecki, K.\u00a0Diedrich, C.\u00a0J. Roth, A.\u00a0Schroeder, N.\u00a0Tenenholtz, K.\u00a0Younis, H.\u00a0Zachmann, Ihe radiology white paper \u2013 ai interoperability in imaging (2021). https:\/\/www.ihe.net\/uploadedFiles\/Documents\/Radiology\/IHE_RAD_White_Paper_AI_Interoperability_in_Imaging.pdf"},{"key":"1856_CR37","unstructured":"I.\u00a0R.\u00a0T. Committee, Technical framework supplement ai workflow for imaging (2020). https:\/\/www.ihe.net\/uploadedFiles\/Documents\/Radiology\/IHE_RAD_Suppl_AIW-I.pdf"},{"key":"1856_CR38","doi-asserted-by":"publisher","unstructured":"R.\u00a0Jesus, J.\u00a0Frias, L.\u00a0B. Silva, C.\u00a0Costa, A Vendor Neutral Archive with MONAI for Automatic Medical Image Analysis, in: Proceedings - IEEE Symposium on Computer-Based Medical Systems, Vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 53\u201356. https:\/\/doi.org\/10.1109\/CBMS58004.2023.00191.","DOI":"10.1109\/CBMS58004.2023.00191"},{"key":"1856_CR39","doi-asserted-by":"publisher","unstructured":"R.\u00a0Jesus, L.\u00a0B. Silva, V.\u00a0Sousa, L.\u00a0Carvalho, D.\u00a0G. Gonzalez, J.\u00a0Carias, C.\u00a0Costa, Personalizable ai platform for universal access to research and diagnosis in digital pathology, Computer Methods and Programs in Biomedicine 242 (2023) 107787. https:\/\/doi.org\/10.1016\/J.CMPB.2023.107787.","DOI":"10.1016\/J.CMPB.2023.107787"},{"key":"1856_CR40","doi-asserted-by":"publisher","unstructured":"R.\u00a0E.\u00a0D. Guerrero, J.\u00a0L. Oliveira, Improvements in lymphocytes detection using deep learning with a preprocessing stage, Proceedings - IEEE Symposium on Computer-Based Medical Systems 2021-June (2021) 178\u2013182. https:\/\/doi.org\/10.1109\/CBMS52027.2021.00068.","DOI":"10.1109\/CBMS52027.2021.00068"},{"key":"1856_CR41","doi-asserted-by":"publisher","unstructured":"D.\u00a0G. Gonzalez, J.\u00a0Carias, Y.\u00a0C. Castilla, J.\u00a0Rodrigues, T.\u00a0Ad\u00e3o, R.\u00a0Jesus, L.\u00a0G.\u00a0M. Magalh\u00e3es, V.\u00a0M.\u00a0L. de\u00a0Sousa, L.\u00a0Carvalho, R.\u00a0Almeida, A.\u00a0Cunha, Evaluating rotation invariant strategies for mitosis detection through yolo algorithms, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 484 LNICST (2023) 24\u201333. https:\/\/doi.org\/10.1007\/978-3-031-32029-3_3.","DOI":"10.1007\/978-3-031-32029-3_3"},{"key":"1856_CR42","doi-asserted-by":"publisher","unstructured":"R.\u00a0Jesus, J.\u00a0Frias, P.\u00a0Gouveia, J.\u00a0Santinha, L.\u00a0B. Silva, C.\u00a0Costa, Dicom gateway anonymizer: A cloud architecture for a scalable research pacs, Proceedings - IEEE Symposium on Computers and Communications (2024). https:\/\/doi.org\/10.1109\/ISCC61673.2024.10733721","DOI":"10.1109\/ISCC61673.2024.10733721"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-01856-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-01856-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-01856-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:55:56Z","timestamp":1771527356000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-01856-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":42,"alternative-id":["1856"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-01856-9","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]},"assertion":[{"value":"17 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study did not involve human participants or animals and therefore did not require ethical approval in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"This study did not involve human participants and therefore did not require consent to participate.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"This study did not involve human participants and therefore did not require consent to publish.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}