Computer Aided Diagnosis for Contrast-Enhanced Ultrasound Using Transformer Neural Network

Today, the Transformer Neural Network (TNN) architecture provides top results in many data processing applications (text, voice, image, and video), outperforming the more traditional convolutional or recurrent Deep Neural Network (DNN) models. To improve AI-based automated diagnosis in the field of medicine, this research seeks to explore TNN capabilities for the characterisation of focal liver lesion (FLL) using contrast-enhanced ultrasound (CEUS). Firstly, our study reviewed TNN architectures used in image classification tasks, and then it aimed at the identification of a suitable TNN variant for the above-mentioned topic. This later aim is justified by the fact that, in a typical case, a TNN works much better when it has a large amount of data available for the training process. Unfortunately, this is not the case for most available CEUS datasets, including the one considered in this paper. We compared our proposal with other solutions based on machine learning reported in the literature and found that it provides comparable accuracy. Moreover, this is done by classifying a higher number of FLL types than most previous CEUS Computer Aided Diagnosis (CAD) systems.