COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

Abstract

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design For Prediction of Pulmonary Fibrosis Progression From Chest CT Images

“…To the best of the authors' knowledge, the notion of explainability-driven performance validation of the decision-making behaviour of artificial intelligence solutions for the prediction of pulmonary fibrosis progression have not been previously explored in literature and can be very valuable for driving greater clinical adoption of such solutions in a transparent and trusted manner. Furthermore, while explainability-driven performance validation strategies has been demonstrated to be very successful in past studies for the purpose of clinical classification [15,17,18,21], the the best of the authors' knowledge this is the first study in literature to successfully leverage explainability-driven performance validation on a clinical regression problem.…”

“…More specifically, the backbone architecture design for CT lung analysis identified via machine-driven design exploration leveraged residual architecture design principles [35,36] as an initial network design prototype, 2,116 patient cases acquired from around the world both with presence and absence of respiratory diseases for improve the quantity and diversity of CT scans, along with associated predictive performance constraints [17,18]. It is upon this backbone architecture design that the proposed Fibrosis-Net network architecture design was built to be tailored specifically for predicting FVC based on the CT scan, initial spirometry measurement, and clinical metadata of a patient.…”

“…The proposed Fibrosis-Net possesses a highly efficient, heterogeneous design comprising largely of lightweight architectural components such as depthwise convolutions and pointwise convolutions. In particular, similar to [17,18], both unstrided and strided projection-replication-projection-expansion design patterns (denoted as PRPE and PRPE-S for unstrided and strided patterns, respectively) are heavily utilized to strike a strong balance between representational capacity and architectural and computational efficiency. The efficiency of the proposed Fibrosis-Net makes it very well-suited for clinical scenarios where computational resources are limited, particularly when dealing with CT scans consisting of many CT images such as the pulmonary fibrosis progression prediction task in this study.…”

“…Inspired by this, we leverage an explainability-driven performance validation strategy to study the decision-making behaviour of Fibrosis-Net as to verify that predictions are based on relevant visual indicators in CT images. Similar to [15,17,18,21], we leverage GSInquire [39] as the explainability method of choice for explainability-driven performance validation in this study to identify critical visual factors in CT images that Fibrosis-Net leverages to make FVC predictions. More specifically, GSInquire harnesses the same generative synthesis strategy [33] leveraged in the machine-driven design exploration process, as a previous study demonstrated the ability of GSInquire to provide explanations that better reflect the decision-making process of deep neural networks quantitatively when compared to other state-of-the-art explainability methods [39].…”

“…Furthermore, to explore the decision-making behaviour of Fibrosis-Net, we leverage an explainability-driven performance validation strategy to audit Fibrosis-Net to verify that predictions are based on relevant visual indicators in CT images. Fibrosis-Net is available to the general public in an open-source and open access manner as part of the OpenMedAI initiative, an open source initiative for medical artificial intelligence solutions that currently include the COVID-Net [15,16,17,18,19] initiative, Cancer-Net [20] initiative, and the TB-Net initiative [21]. While Fibrosis-Net is not yet a production-ready screening solution, we hope that its open source release will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.…”