{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:46:45Z","timestamp":1760060805594,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia, IP (FCT)","award":["UIDB\/00319\/2020 (ALGORITMI)"],"award-info":[{"award-number":["UIDB\/00319\/2020 (ALGORITMI)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The collection and annotation of data for supervised machine learning remain challenging and costly tasks, particularly in domains that demand expert knowledge. Depending on the application, labelling may require highly specialised professionals, significantly increasing the overall effort and expense. Active learning techniques offer a promising solution by reducing the number of annotations needed, thereby lowering costs without compromising model performance. This work proposes an active learning with a decreasing-budget-based strategy to reduce the effort required to annotate medical images. The strategy encourages data annotators to focus on initial iterations, optimise budget allocation, and ensure that the trained model achieves maximum performance with reduced effort in subsequent iterations. This strategy also improves the performance of deep learning models, which perform better with fewer images, reducing the specialists\u2019 workload. This work also introduces three experiments that contribute to understanding the impact of the strategy in the annotation process.<\/jats:p>","DOI":"10.3390\/app151810195","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T14:45:46Z","timestamp":1758206746000},"page":"10195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimising Active Learning with a Decreasing-Budget-Based Strategy: A Medical Application Case Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9803-1670","authenticated-orcid":false,"given":"Dibet","family":"Garcia Gonzalez","sequence":"first","affiliation":[{"name":"Sentinelconcept, LDA. F\u00e1brica ASA, Estrada Nacional 105, Rua de Covas, Piso 1, Sala J1.3, 4835-164 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9920-2133","authenticated-orcid":false,"given":"Maria In\u00eas","family":"Leite","sequence":"additional","affiliation":[{"name":"Department of Engineering, School of Sciences and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4426-0002","authenticated-orcid":false,"given":"Luis","family":"Magalh\u00e3es","sequence":"additional","affiliation":[{"name":"ALGORITMI Research Centre, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7693","authenticated-orcid":false,"given":"Antonio","family":"Cunha","sequence":"additional","affiliation":[{"name":"Department of Engineering, School of Sciences and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"ALGORITMI Research Centre, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Steiner, D.F., Chen, P.H.C., and Mermel, C.H. 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