Classifier Fusion for Detection of COVID-19 from CT Scans.

The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 is found to be the most infectious disease in last few decades. This disease has infected millions of people worldwide. The inadequate availability and the limited sensitivity of the testing kits have motivated the clinicians and the scientist to use Computer Tomography (CT) scans to screen COVID-19. Recent advances in technology and the availability of deep learning approaches have proved to be very promising in detecting COVID-19 with increased accuracy. However, deep learning approaches require a huge labeled training dataset, and the current availability of benchmark COVID-19 data is still small. For the limited training data scenario, the CNN usually overfits after several iterations. Hence, in this work, we have investigated different pre-trained network architectures with transfer learning for COVID-19 detection that can work even on a small medical imaging dataset. Various variants of the pre-trained ResNet model, namely ResNet18, ResNet50, and ResNet101, are investigated in the current paper for the detection of COVID-19. The experimental results reveal that transfer learned ResNet50 model outperformed other models by achieving a recall of 98.80% and an F1-score of 98.41%. To further improvise the results, the activations from different layers of best performing model are also explored for the detection using the support vector machine, logistic regression and K-nearest neighbor classifiers. Moreover, a classifier fusion strategy is also proposed that fuses the predictions from the different classifiers via majority voting. Experimental results reveal that via using learned image features and classification fusion strategy, the recall, and F1-score have improvised to 99.20% and 99.40%.

The Impact of COVID-19 on Academic Cancer Clinical Trials in Canada and the Initial Response from Cancer Centers.

The COVID-19 pandemic resulted in temporary holds placed on new trial startups, patient recruitment and follow up visits for trials which contributed to major disruptions in cancer center trial unit operations. To assess the impact, the Canadian Cancer Clinical Trials Network (3CTN) members participated in regional meetings and a survey to understand the impact of the pandemic to academic cancer clinical trials (ACCT) activity, cancer trial unit operations and supports needed for post-pandemic recovery. Trial performance and recruitment data collected from 1 April 2020-31 March 2021 was compared to the same period in previous years. From 1 April-30 June 2020, patient recruitment decreased by 67.5% and trial site activations decreased by 81% compared to the same period in 2019. Recovery to reopening and recruitment of ACCTs began after three months, which was faster than initially projected. However, ongoing COVID-19 impacts on trial unit staffing and operations continue to contribute to delayed trial activations, lower patient recruitment and may further strain centers' capacity for participation in academic-sponsored trials.