Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

Amine Amyar; Romain Modzelewski; Su Ruan

In this paper, we proposed a multi-task learning approach to detect COVID-19 from CT images and segment the regions of interest simultaneously. Our method can improve the segmentation results even if we do no have many segmentation ground truths, thanks to the classification data with ground truth which can be easily obtained compared to that of segmentation. Our method shows very promising results. It outperformed the state of the art methods for image segmentation when used alone such as U-NET or image classification such as CNNs. We have shown that by combining jointly these two tasks, the method improves for both segmentation and classification performances. Moreover, adding a third task such as image reconstruction, the encoder can extract meaningful feature representation which can help the other tasks (classification and segmentation) to improve even more their performances.