Automatic Clustering of CT Scans of COVID-19 Patients Based on Deep Learning

Although several vaccination campaigns have been launched to combat the ongoing COVID-19 pandemic, the primary treatment of suspected infected people is still symptomatic. In particular, the analysis of images derived from computed tomography (CT) appears to be useful for retrospectively analyzing the novel coronavirus and the chest injuries it causes. The growing body of literature on this topic shows the predominance of supervised learning methods that are typically adopted to automatically discriminate pathological patients from normal controls. However, very little work has been done from an unsupervised perspective. In this paper, we propose a new pipeline for automatic clustering of CT scans of COVID-19 patients based on deep learning. A pre-trained convolutional neural network is used for feature extraction; then, the extracted features are used as input to a deep embedding clustering model to perform the final clustering. The method was tested on the publicly available SARS-CoV-2 CT-Scan dataset that not only provides scans of COVID patients but also of patients with other lung conditions. The results obtained indicate that the radiological features of COVID patients largely overlap with those of other lung diseases. Unsupervised approaches to COVID analysis are promising, as they reduce the need for hard-to-collect human annotations and allow for deeper analysis not tied to a binary or multiclass classification task.