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

Amine Amyar; Romain Modzelewski; Su Ruan

In [23] , Jin et al. created and deployed an AI tool to analyze CT images of COVID-19 in 4 weeks. To do this, a multidisciplinary team of 30 people collaborated together using a database of 1136 images including 723 positive COVID-19 images from five hospitals, to achieve a sensitivity of 0.974 and a specificity of 0.922. The system was deployed in 16 hospitals and performed over 1300 screenings per day. They proposed a combined model for classification and segmentation showing lesion regions in addition to the screening results. The pipeline is divided into 2 steps: segmentation and classification. They used several models including 3D U-NET++, V-NET, FCN-8S for segmentation and Incep-tionV3, ResNet50 and others for classification. They were able to achieve a dice coefficient of 0.754 using 3D U-NET ++ trained on 732 cases. The combination of 3D U-NET ++ and ResNet50 resulted in an area under the OCR curve of 0.991 with a sensitivity of 0.974 and a specificity of 0.922. In practice, the model continued to improve by re-training. The model proved to be very useful to physicians by highlighting regions of lesions which improved the diagnosis. What should be noted here is that the two models are independent and therefore they cannot help each other to improve both classification and segmentation performances.