{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:29Z","timestamp":1750309529781,"version":"3.41.0"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Health from Portugal - Agenda Mobilizadora para a Inova\u00e7\u00e3o Empresarial"},{"name":"Plano de Recupera\u00e7\u00e3o e Resili\u00eancia portugu\u00eas","award":["C644937233-00000047"],"award-info":[{"award-number":["C644937233-00000047"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Data and Information Quality"],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>Deep learning algorithms have become increasingly popular over the years, having proved their efficiency in input-output functions for distinct types of data. This technology is particularly useful in medical imaging, where complex image structures often generate disagreements between medical staff. These technologies can streamline the diagnostic process by performing automatic image analysis, which results in more accurate and reproducible diagnoses. Additionally, these technologies can enhance content retrieval systems by automatically labeling the images based on the structures they possess. Despite the benefits, the mathematical complexity of deep learning algorithms and their training optimizations can be challenging. Automated machine learning provides a solution to this challenge by offering tools that automate the development and training of these algorithms. This makes it possible for users with limited programming experience to take advantage of these powerful technologies to quickly develop and prototype analysis algorithms for their specific needs. This article presents a management platform for deep learning services on the cloud that provides a code-free experience through automated machine learning. The evaluation was done in one of the most demanding scenarios, where the service was integrated into a research pathology PACS to annotate mitotic cells in breast cancer tissue automatically. The annotations are processed by an open-source PACS archive and stored directly on the files, enhancing the image metadata and consequently content retrieval systems. The results of the developed algorithms were compared to the state-of-the-art to evaluate the competitiveness of the solution.<\/jats:p>","DOI":"10.1145\/3705896","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T10:03:17Z","timestamp":1732615397000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Unlocking AutoML: Enhancing Data with Deep Learning Algorithms for Medical Imaging"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9231-6744","authenticated-orcid":false,"given":"Rui Filipe","family":"Ribeiro Jesus","sequence":"first","affiliation":[{"name":"Faculty of Informatics, University of A Coruna, A Coruna, Spain"},{"name":"BMD Software, Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8856-8601","authenticated-orcid":false,"given":"Ana","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Physics Department, University of Aveiro, Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2707-5331","authenticated-orcid":false,"given":"Carlos","family":"Costa","sequence":"additional","affiliation":[{"name":"IEETA\/DETI, University of Aveiro, Aveiro, Portugal"}]}],"member":"320","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Mart\u00edn Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry Moore Derek G. Murray Benoit Steiner Paul Tucker Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2016. TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation OSDI 2016 (May 2016) 265\u2013283. Retrieved from https:\/\/arxiv.org\/abs\/1605.08695v2"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528120"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.MEDIA.2019.05.010"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93000-8_89"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-92bf1922-003"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759461"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1609\/AAAI.V30I1.10140"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447556.3447567"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","unstructured":"David A. Clunie. 2020. DICOM Format and Protocol Standardization\u2014A Core Requirement for Digital Pathology Success. Toxicologic Pathology 49 4 (October 2020) 738\u2013749. DOI:10.1177\/0192623320965893","DOI":"10.1177\/0192623320965893"},{"key":"e_1_3_1_11_2","unstructured":"R. Collobert K. Kavukcuoglu and C. Farabet. 2011. Torch7: A Matlab-like environment for machine learning. Neural Information Processing Systems (2011). Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:14365368"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2020.00004"},{"key":"e_1_3_1_13_2","article-title":"Automated machine learning\u2014A brief review at the end of the early years","author":"Escalante Hugo Jair","year":"2020","unstructured":"Hugo Jair Escalante. 2020. Automated machine learning\u2014A brief review at the end of the early years. arXiv preprint arXiv:2008.08516 (Aug2020).","journal-title":"arXiv preprint arXiv:2008.08516"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","unstructured":"Jo\u00e3o Ferreira and Carlos Costa. 2021. Web platform for medical deep learning services. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 1727\u20131732. DOI:10.1109\/BIBM52615.2021.9669704","DOI":"10.1109\/BIBM52615.2021.9669704"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/S10462-023-10453-Z\/TABLES\/10"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.KNOSYS.2020.106622"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/info11020108"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","unstructured":"Yangqing Jia Evan Shelhamer Jeff Donahue Sergey Karayev Jonathan Long Ross Girshick Sergio Guadarrama and Trevor Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding. MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia (June 2014) 675\u2013678. DOI:10.1145\/2647868.2654889","DOI":"10.1145\/2647868.2654889"},{"issue":"6","key":"e_1_3_1_19_2","first-page":"1","article-title":"AutoKeras: An AutoML library for deep learning","volume":"24","author":"Jin Haifeng","year":"2023","unstructured":"Haifeng Jin, Fran\u00e7ois Chollet, Qingquan Song, and Xia Hu. 2023. AutoKeras: An AutoML library for deep learning. J. Mach. Learn. Res. 24, 6 (2023), 1\u20136. Retrieved from http:\/\/jmlr.org\/papers\/v24\/20-1355.html","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2016.54"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/S10916-022-01867-3"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.112693"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","unstructured":"Forough Majidi Moses Openja Foutse Khomh and Heng Li. 2022. An empirical study on the usage of automated machine learning tools. Proceedings - 2022 IEEE International Conference on Software Maintenance and Evolution ICSME 2022 (August 2022) 59\u201370. DOI:10.1109\/ICSME55016.2022.00014","DOI":"10.1109\/ICSME55016.2022.00014"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-3516-4_2"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/S11263-010-0392-0\/METRICS"},{"issue":"4","key":"e_1_3_1_26_2","article-title":"A review of deep learning research","volume":"13","author":"Mu Ruihui","year":"2019","unstructured":"Ruihui Mu and Xiaoqin Zeng. 2019. A review of deep learning research. KSII Trans. Internet Inf. Syst. 13, 4 (2019).","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93000-8_99"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICISC.2017.8068684"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.74940"},{"key":"e_1_3_1_30_2","unstructured":"Daniel Racoceanu J. Calvo Elham Attieh G. L. Naour and A. Gloaguen. 2014. Detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images. In 22nd International Conference on Pattern Recognition. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:43730491"},{"key":"e_1_3_1_31_2","unstructured":"Siddhant Rao. 2018. MITOS-RCNN: A novel approach to mitotic figure detection in breast cancer histopathology images using region based convolutional neural networks. (72018). Retrieved from https:\/\/arxiv.org\/abs\/1807.01788v1"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMPMEDIMAG.2018.11.003"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.BBE.2018.10.007"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMPMEDIMAG.2017.12.001"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMPBIOMED.2020.104129"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3470918"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105596"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCUBEA.2018.8697857"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2912200"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2820199"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2019.00209"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.MEDIA.2019.02.012"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","unstructured":"Mitko Veta Josien P. W. Pluim Paul J. Van Diest and Max A. Viergever. 2014. Breast cancer histopathology image analysis: A review. IEEE Transactions on Biomedical Engineering 61 5 (May 2014) 1400\u20131411. DOI:10.1109\/TBME.2014.2303852","DOI":"10.1109\/TBME.2014.2303852"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1093\/JMICRO\/DFZ002"},{"key":"e_1_3_1_45_2","article-title":"How much automation does a data scientist want?","author":"Wang Dakuo","year":"2021","unstructured":"Dakuo Wang, Q. Vera Liao, Yunfeng Zhang, Udayan Khurana, Horst Samulowitz, Soya Park, Michael Muller, and Lisa Amini. 2021. How much automation does a data scientist want? arXiv preprint arXiv:2101.03970 (Jan.2021).","journal-title":"arXiv preprint arXiv:2101.03970"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","unstructured":"Haibo Wang Angel Cruz-Roa Ajay Basavanhally Hannah Gilmore Natalie Shih Mike Feldman John Tomaszewski Fabio Gonzalez and Anant Madabhushi. 2014. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. SPIE 9041 (March 2014) 66\u201375. DOI:10.1117\/12.2043902","DOI":"10.1117\/12.2043902"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445306"}],"container-title":["Journal of Data and Information Quality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705896","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3705896","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:13Z","timestamp":1750295893000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705896"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,11]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12,31]]}},"alternative-id":["10.1145\/3705896"],"URL":"https:\/\/doi.org\/10.1145\/3705896","relation":{},"ISSN":["1936-1955","1936-1963"],"issn-type":[{"type":"print","value":"1936-1955"},{"type":"electronic","value":"1936-1963"}],"subject":[],"published":{"date-parts":[[2024,12,11]]},"assertion":[{"value":"2023-05-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-28","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}