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.
@article{arxiv.2308.01137,
title = {Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans},
author = {Weronika Hryniewska-Guzik and Maria Kędzierska and Przemysław Biecek},
journal= {arXiv preprint arXiv:2308.01137},
year = {2024}
}
Comments
presented at the Polish Conference on Artificial Intelligence (PP-RAI), 2023