Multi-task learning for classification, segmentation, reconstruction,
  and detection on chest CT scans
Weronika Hryniewska-Guzik Maria Kędzierska Przemysław Biecek
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.