4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection

Abstract

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for Transfer learning (4S-DT) model. 4S-DT encourages a coarseto-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabelled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a classdecomposition layer to simplify the local structure of the data. 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream classdecomposition mechanism. We used 50,000 unlabelled chest Xray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. 4S-DT has achieved a high accuracy of 99.8% (95% CI: 99.44 %, 99.98%) in the detection of COVID-19 cases on a large dataset and an accuracy of 97.54% (95% CI: 96.22%, 98.91%) on an extended test set enriched by augmented images of a small dataset, out of which all real COVID-19 cases were detected, which was the highest accuracy obtained when compared to other methods.

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

“…With the emerge of deep neural networks (DNNs) [ [9] , [10] , [11] , [12] , [13] , [14] ], especially convolutional neural networks (CNNs), they leverage multi-level layer neural networks for representational learning and are widely used for image classification [ 15 , 16 ], object detection [ 17 , 18 ] and semantic segmentation [ 19 ]. Naturally, DNNs are very good at detecting COVID-19 [ [20] , [21] , [22] , [23] , [24] , [25] ]. However, mainly due to lack of interpretability and practical skills, applying DNNs into CXR images for COVID-19 clinical diagnosis has run into obstacles [ 26 ].…”

A Survey on Deep Learning Advances and Emerging Issues in Pneumonia and COVID19 Prediction

“…Abbas et al in [70] applied a self-supervised learning algorithm to predict COVID19 in CXR images. A combination of deep CNNs, transfer learning, clustering algorithms gave 99.8% on two massive datasets by feeding unlabeled images.…”

A systematic comparison of transfer learning models for COVID-19 prediction

“…Using deep learning technology, a number of research studies have been conducted to investigate automated radiographs of the lungs to distinguish patients with pneumonia and COVID-19. Many authors investigated the performance of transfer learning approaches for COVID-19 screening [12,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Narin et al [15], for example, created three CNN-based models using the current transfer learning architecture and found that the ResNet model had the best classification accuracy.…”