ABSTRACT
The wide spread of coronavirus disease 2019 (COVID-19) has become a global concern and millions of people have been infected. Chest Computed Tomography (CT) imaging is important for screening and diagnosis of this disease, where segmentation of the lung infections plays a critical role for quantitative assessment of the disease progression. Currently, 3D Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation tasks. However, most 3D segmentation CNNs have a large set of parameters and huge floating point operations (FLOPs), causing high command for equipments. In this work, we propose LCOV-Net, a lightweight 3D CNN for accurate segmentation of COVID-19 pneumonia lesions from CT volumes. The core component of LCOV-Net is a lightweight attention-based convolutional block (LACB), which consists of a spatiotemporal separable convolution branch to reduce parameters and a lightweight feature calibration branch to improve the learning ability. We combined our LACB module with 3D U-Net as LCOV-Net, and tested our method on a dataset of CT scans of 130 COVID-19 patients for the infection lesion segmentation. Experimental results show that: (1) our LCOV-Net outperforms existing lightweight networks for 3D segmentation and (2) compared with the widely used 3D U-Net, our LCOV-Net improved the Dice score by around 20.36% and reduced the parameter number by 90.16%, leading to 27.93% speedup. Models and code are available at https://github.com/afeizqf/LCOVNet.
ABSTRACT
Purpose Amid the coronavirus disease 2019 (COVID-19) pandemic, higher education institutions (HEI) all over the world have transitioned to online teaching. The purpose of this study is to examine the impact of technostress and negative emotional dissonance on online teaching exhaustion and teaching staff productivity. Design/methodology/approach Survey methodology was used to collect data from faculty members in Jordanian universities. A total of 217 responses were analyzed to test the research model. Findings The research findings reveal that technostress creators have various impact on online teaching exhaustion and teaching staff productivity. Negative emotional dissonance has positive impact on both online teaching exhaustion and teaching staff productivity. Further, online teaching exhaustion is negatively associated with teaching staff productivity. Research limitations/implications This research extends prior literature on technostress by examining the phenomenon in abnormal conditions (during a crisis). It further integrates technostress theory with emotional dissonance theory to better understand the impact of technostress creators on individual teaching staff productivity while catering for the interactional nature of teaching which is captured through emotional dissonance theory. Practical implications The research offers valuable insights for HEI and policymakers on how to support teaching staff and identifies strategies that should facilitate a smooth delivery of online education. Originality/value Unlike prior research that have examined technostress under normal operational conditions, this research examines the impact of technostress during a crisis. This study shows that technostress creators vary in their impact. Moreover, this study integrates technostress theory with emotional dissonance theory. While technostress theory captures the impact of technostress creators on individual teaching staff productivity, emotional dissonance theory captures the dynamic nature of the teaching process that involves interactions among teachers and students.