  The novel Coronavirus disease, COVID-19, has rapidly and abruptly changed the
world as we knew in 2020. It becomes the most unprecedent challenge to analytic
epidemiology in general and signal processing theories in specific. Given its
high contingency nature and adverse effects across the world, it is important
to develop efficient processing/learning models to overcome this pandemic and
be prepared for potential future ones. In this regard, medical imaging plays an
important role for the management of COVID-19. Human-centered interpretation of
medical images is, however, tedious and can be subjective. This has resulted in
a surge of interest to develop Radiomics models for analysis and interpretation
of medical images. Signal Processing (SP) and Deep Learning (DL) models can
assist in development of robust Radiomics solutions for diagnosis/prognosis,
severity assessment, treatment response, and monitoring of COVID-19 patients.
In this article, we aim to present an overview of the current state,
challenges, and opportunities of developing SP/DL-empowered models for
diagnosis (screening/monitoring) and prognosis (outcome prediction and severity
assessment) of COVID-19 infection. More specifically, the article starts by
elaborating the latest development on the theoretical framework of analytic
epidemiology and hypersignal processing for COVID-19. Afterwards, imaging
modalities and Radiological characteristics of COVID-19 are discussed.
SL/DL-based Radiomic models specific to the analysis of COVID-19 infection are
then described covering the following four domains: Segmentation of COVID-19
lesions; Predictive models for outcome prediction; Severity assessment, and;
Diagnosis/classification models. Finally, open problems and opportunities are
presented in detail.
