{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:29:46Z","timestamp":1778081386925,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,28]]},"abstract":"<jats:p>Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available.<\/jats:p>","DOI":"10.3233\/faia230518","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:19:12Z","timestamp":1695979152000},"source":"Crossref","is-referenced-by-count":3,"title":["ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4006-356X","authenticated-orcid":false,"given":"\u0141ukasz","family":"Struski","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Jagiellonian University"},{"name":"UES Ltd."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8543-5200","authenticated-orcid":false,"given":"Dawid","family":"Rymarczyk","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Jagiellonian University"},{"name":"Doctoral School of Exact and Natural Sciences, Jagiellonian University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1922-1562","authenticated-orcid":false,"given":"Arkadiusz","family":"Lewicki","sequence":"additional","affiliation":[{"name":"Faculty of Applied Computer Science, University of Information Technology and Management in Rzeszow"},{"name":"UES Ltd."}]},{"given":"Robert","family":"Sabiniewicz","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology and Congenital Heart Diseases, Medical University of Gdansk"},{"name":"UES Ltd."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6652-7727","authenticated-orcid":false,"given":"Jacek","family":"Tabor","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Jagiellonian University"},{"name":"UES Ltd."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-3621","authenticated-orcid":false,"given":"Bartosz","family":"Zieli\u0144ski","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Jagiellonian University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230518","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:19:14Z","timestamp":1695979154000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230518","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}