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arXiv · 2308.01137

Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans

Weronika Hryniewska-Guzik·Maria Kędzierska·Przemysław Biecek
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Abstract

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.

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Weronika Hryniewska-Guzik, Maria Kędzierska, Przemysław Biecek. 2023-08-02. Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans. https://doi.org/10.34658/9788366741928.40

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