Multi task deep learning based CT imaging analysis for COVID 19 pneumonia: Classification and segmentation
This paper presents an automatic classification segmentation tool for helping screening COVID 19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID 19 patient and segment COVID 19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID 19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification.
Computers in Biology and Medicine