AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19

Abstract

In recent years, deep learning techniques have been widely used to diagnose diseases. However, in some tasks, such as the diagnosis of COVID-19 disease, due to insufficient data, the model is not properly trained and as a result, the generalizability of the model decreases. For example, if the model is trained on a CT scan dataset and tested on another CT scan dataset, it predicts near-random results. To address this, data from several different sources can be combined using transfer learning, taking into account the intrinsic and natural differences in existing datasets obtained with different medical imaging tools and approaches. In this paper, to improve the transfer learning technique and better generalizability between multiple data sources, we propose a multi-source adversarial transfer learning model, namely AMTLDC. In AMTLDC, representations are learned that are similar among the sources. In other words, extracted representations are general and not dependent on the particular dataset domain. We apply the AMTLDC to predict Covid-19 from medical images using a convolutional neural network. We show that accuracy can be improved using the AMTLDC framework, and surpass the results of current successful transfer learning approaches. In particular, we show that the AMTLDC works well when using different dataset domains, or when there is insufficient data.

A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays

“…It was generalizable between multiple data sources. The accuracy of AMTLDC surpasses the other existing pre-trained models [ 32 ]. Kumar et al design a proposed ensemble model which detects COVID-19 infection by integrating various transfer learning models such as GoogLeNet, EfficientNet, and XceptionNet.…”

Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification

“…Additionally, the results from the multi-source methods are superior to those from the source-combine methods, indicating that utilizing the information from multiple source centers enhances the model generalization and classification accuracy more effectively than relying on a single source center's data. Compared with DANNs [31] and MCD [19], two single-source domain adaptation methods, the WUCF also showed improved performance. The major difference between the two types is that the data features are mapped to a common space in DANNs and MCD, whereas the WUCF performs targeted feature mapping, and the domain expert discriminator can more effectively and flexibly integrate this independent "knowledge".…”