  This paper presents a novel lightweight COVID-19 diagnosis framework using CT
scans. Our system utilises a novel two-stage approach to generate robust and
efficient diagnoses across heterogeneous patient level inputs. We use a
powerful backbone network as a feature extractor to capture discriminative
slice-level features. These features are aggregated by a lightweight network to
obtain a patient level diagnosis. The aggregation network is carefully designed
to have a small number of trainable parameters while also possessing sufficient
capacity to generalise to diverse variations within different CT volumes and to
adapt to noise introduced during the data acquisition. We achieve a significant
performance increase over the baselines when benchmarked on the SPGC COVID-19
Radiomics Dataset, despite having only 2.5 million trainable parameters and
requiring only 0.623 seconds on average to process a single patient's CT volume
using an Nvidia-GeForce RTX 2080 GPU.
