Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images

Abstract

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal’s, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance.

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

“… TL? Scarpiniti, Sarv Ahrabi [ 68 ] A histogram-based 90.1 90.3 90.4 91 No No Perumal, Narayanan [ 69 ] CNN 92.3 91.5 92.6 93 No No Uemura, Näppi [ 70 ] GAN 95.1 95.4 96 95.3 No No Zhao, Xu [ 71 ] 3D V-Net 97.4 97.7 97.2 98.7 No No Hu, Huang [ 72 ] DNN 97.2 97.1 98.2 99 No No Toğaçar, Muzoğlu [ 73 ] CNN 97.6 97.3 98.1 99.1 No No Castiglione, Vijayakumar [ 74 ] ADECO-CNN 98.2 98.6 98.4 99 No Yes …”

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

“…Similarly, a few works 235 , 236 , 237 , 238 , 239 successfully induced the self-supervised intuition for COVID-19 diagnosis. Taking the discussion as mentioned above into account, self-supervised learning has the protentional and can be applied successfully.…”

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

“…Using CT imaging and clinical data, a DL model successfully predicted the time until progression to critical illness in individual patients while identifying high-risk patients [54]. Deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) was demonstrated to be effective for the diagnosis of COVID-19 with varying degrees of data imbalance [55]. The DL-based radiomics features of pulmonary opacities on chest CT images were superior to subjective assessments in differentiating patients with favorable and adverse outcomes [56].…”