A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging

Such applications are, for example, 3-D chest CT scan analysis for pneumonia, COVID-19, or Lung cancer diagnosis [1], [2]; 3-D magnetic resonance image (MRI) analysis for Parkinson's, or Alzheimer's disease prediction [3], [4]; 3-D subject's movement analysis for action recognition & Parkinson's detection [5]; analysis of audiovisual data showing subject's behaviour for affect recognition [6]; anomaly detection in nuclear power plants [7]. Dealing with a single annotation per volumetric input and harmonizing the input variable length constitutes a significant problem when training Deep Neural Networks (DNNs) to perform the respective prediction, or classification task. Furthermore, in each of the above application fields, public, or private datasets are produced in different environments and contexts and are used to train deep learning systems to successfully perform the respective tasks. Extensive research is currently made on using data-driven knowledge, extracted from a single, or from multiple datasets, so as to deal with other datasets. Transfer learning, domain adaptation, meta-learning, domain generalization, continual or life long learning are specific topics of this research, based on different conditions related to the considered datasets [8].