Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans
This work addresses the challenge of early and time-consuming lesion identification for physicians in medical imaging, but it appears incremental as it adds detection to existing multi-task solutions.
The authors tackled the problem of identifying lesions in chest CT scans for lung cancer and COVID-19 by proposing a novel multi-task learning framework that includes classification, segmentation, reconstruction, and detection, achieving unspecified results without concrete numbers.
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.