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

• URL: http://arxiv.org/abs/2308.01137v1
• Date: Wed, 2 Aug 2023 13:28:44 GMT
• Title: Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans
• Authors: Weronika Hryniewska-Guzik, Maria K\k{e}dzierska, Przemys{\l}aw Biecek
• Abstract summary: Lung cancer and covid-19 have one of the highest morbidity and mortality rates in the world. We propose a novel multi-task framework for classification, segmentation, reconstruction, and detection.
• Score: 4.91155110560629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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