A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis
COVID-19 has caused a global pandemic and become the most urgent threat to the entire
world. Tremendous efforts and resources have been invested in developing diagnosis …
world. Tremendous efforts and resources have been invested in developing diagnosis …
Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A …
Abstract Background COVLIAS 1.0: an automated lung segmentation was designed for
COVID-19 diagnosis. It has issues related to storage space and speed. This study shows …
COVID-19 diagnosis. It has issues related to storage space and speed. This study shows …
ADU-Net: an attention dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images
An automatic method for qualitative and quantitative evaluation of chest Computed
Tomography (CT) images is essential for diagnosing COVID-19 patients. We aim to develop …
Tomography (CT) images is essential for diagnosing COVID-19 patients. We aim to develop …
Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images
The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an
imperative to develop accurate and efficient diagnostic tools for the disease as medical …
imperative to develop accurate and efficient diagnostic tools for the disease as medical …
Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation
Abstract Background and Objective: Coronavirus disease 2019 (COVID-19) is a highly
contagious virus spreading all around the world. Deep learning has been adopted as an …
contagious virus spreading all around the world. Deep learning has been adopted as an …
Deep learning models for COVID-19 infected area segmentation in CT images
A Voulodimos, E Protopapadakis… - Proceedings of the 14th …, 2021 - dl.acm.org
Recent studies indicated that detecting radiographic patterns on CT chest scans can yield
high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the …
high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the …
Mt-ncov-net: a multitask deep-learning framework for efficient diagnosis of covid-19 using tomography scans
W Ding, M Abdel-Basset, H Hawash… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The localization and segmentation of the novel coronavirus disease of 2019 (COVID-19)
lesions from computerized tomography (CT) scans are of great significance for developing …
lesions from computerized tomography (CT) scans are of great significance for developing …
[HTML][HTML] COVID TV-Unet: Segmenting COVID-19 chest CT images using connectivity imposed Unet
The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more
than 200 countries around the world, leading to a severe impact on the health and life of …
than 200 countries around the world, leading to a severe impact on the health and life of …
An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors
L Zhou, X Meng, Y Huang, K Kang, J Zhou… - Nature Machine …, 2022 - nature.com
Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but
knowledge on long-term complications is limited. In particular, a large portion of survivors …
knowledge on long-term complications is limited. In particular, a large portion of survivors …
[HTML][HTML] COVLIAS 1.0 vs. MedSeg: artificial intelligence-based comparative study for automated COVID-19 computed tomography lung segmentation in Italian and …
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for
COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were …
COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were …