Hryniewska-Guzik, WeronikaKędzierska, MariaBiecek, Przemysław2023-09-222023-09-222023Hryniewska-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: 10.34658/9788366741928.40.978-83-66741-92-8http://hdl.handle.net/11652/4816https://doi.org/10.34658/9788366741928.40Lung 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.enDla wszystkich w zakresie dozwolonego użytkuFair use conditionmulti-task learningcomputed tomographydetectionuczenie się wielozadaniowetomografia komputerowadetekcjaRozdział - monografiaLicencja PŁLUT License10.34658/9788366741928.40