An Explainable Medical Imaging Framework for Modality Classifications Trained Using Small Datasets

With the huge expansion of artificial intelligence in medical imaging, many clinical warehouses, medical centres and research communities, have organized patients’ data in well-structured datasets. These datasets are one of the key elements to train AI-enabled solutions. Additionally, the value of such datasets depends on the quality of the underlying data. To maintain the desired high-quality standard, these datasets are actively cleaned and continuously expanded. This labelling process is time-consuming and requires clinical expertise even when a simple classification task must be performed. Therefore, in this work, we propose to tackle this problem by developing a new pipeline for the modality classification of medical images. Our pipeline has the purpose to provide an initial step in organizing a large collection of data and grouping them by modality, thus reducing the involvement of costly human raters. In our experiments, we consider 4 popular deep neural networks as the core engine of the proposed system. The results show that when limited datasets are available simpler pre-trained networks achieved better results than more complex and sophisticated architectures. We demonstrate this by comparing the considered networks on the ADNI dataset and by exploiting explainable AI techniques that help us to understand our hypothesis. Still today, many medical imaging studies make use of limited datasets, therefore we believe that our contribution is particularly relevant to drive future developments of new medical imaging technologies when limited data are available.