Multi-task Learning for Classification, Segmentation, Reconstruction, and Detection on Chest CT Scans

Uproszczony widok
dc.contributor.authorHryniewska-Guzik, Weronika
dc.contributor.authorKędzierska, Maria
dc.contributor.authorBiecek, Przemysław
dc.date.accessioned2023-09-22T10:05:19Z
dc.date.available2023-09-22T10:05:19Z
dc.date.issued2023
dc.description.abstractLung cancer and COVID-19 have one of the highest morbidity and mortality rates in the world. For physicians, the identification of lesions is difficult in the early stages of the disease and time-consuming. Therefore, multi-task learning is an approach to extracting important features, such as lesions, from small amounts of medical data because it learns to generalize better. We propose a novel multi-task framework for classification, segmentation, reconstruction, and detection. To the best of our knowledge, we are the first ones who added detection to the multi-task solution. Additionally, we checked the possibility of using two different backbones and different loss functions in the segmentation task.en_EN
dc.identifier.citationHryniewska-Guzik W., Kędzierska M., Biecek P., Multi-task Learning for Classification, Segmentation, Reconstruction, and Detection on Chest CT Scans. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 251-257, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.40.
dc.identifier.doi10.34658/9788366741928.40
dc.identifier.isbn978-83-66741-92-8
dc.identifier.urihttp://hdl.handle.net/11652/4816
dc.identifier.urihttps://doi.org/10.34658/9788366741928.40
dc.language.isoenen_EN
dc.page.numbers. 251-257
dc.publisherWydawnictwo Politechniki Łódzkiejpl_PL
dc.publisherLodz University of Technology Pressen_EN
dc.relation.ispartofWojciechowski A. (Ed.), Lipiński P. (Ed.)., Progress in Polish Artificial Intelligence Research 4, Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.
dc.rightsDla wszystkich w zakresie dozwolonego użytkupl_PL
dc.rightsFair use conditionen_EN
dc.rights.licenseLicencja PŁpl_PL
dc.rights.licenseLUT Licenseen_EN
dc.subjectmulti-task learningen_EN
dc.subjectcomputed tomographyen_EN
dc.subjectdetectionen_EN
dc.subjectuczenie się wielozadaniowepl_PL
dc.subjecttomografia komputerowapl_PL
dc.subjectdetekcjapl_PL
dc.titleMulti-task Learning for Classification, Segmentation, Reconstruction, and Detection on Chest CT Scansen_EN
dc.typeRozdział - monografiapl_PL
dc.typeChapter - monographen_EN

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