Multi-task Learning for Classification, Segmentation, Reconstruction, and Detection on Chest CT Scans
Ładowanie...
Data
2023
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
Lung 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.
Opis
Słowa kluczowe
multi-task learning, computed tomography, detection, uczenie się wielozadaniowe, tomografia komputerowa, detekcja
Cytowanie
Hryniewska-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.