A SARS-CoV-2 Microscopic Image Dataset with Ground Truth Images and Visual Features

@inproceedings{Li2020ASM,
  title={A SARS-CoV-2 Microscopic Image Dataset with Ground Truth Images and Visual Features},
  author={Chen Li and Jiawei Zhang and Frank Kulwa and Shouliang Qi and Ziyu Qi},
  booktitle={PRCV},
  year={2020}
}
SARS-CoV-2 has characteristics of wide contagion and quick propagation velocity. To analyse the visual information of it, we build a SARS-CoV-2 Microscopic Image Dataset (SC2-MID) with 48 electron microscopic images and also prepare their ground truth images. Furthermore, we extract multiple classical features and novel deep learning features to describe the visual information of SARS-CoV-2. Finally, it is proved that the visual features of the SARS-CoV-2 images which are observed under the… 

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References

SHOWING 1-10 OF 20 REFERENCES

Xception: Deep Learning with Depthwise Separable Convolutions

  • François Chollet
  • Computer Science
    2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
TLDR
This work proposes a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions, and shows that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset, and significantly outperforms it on a larger image classification dataset.

Very Deep Convolutional Networks for Large-Scale Image Recognition

TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

Histograms of oriented gradients for human detection

  • N. DalalB. Triggs
  • Computer Science
    2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • 2005
TLDR
It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.

A Review of Clustering Methods in Microorganism Image Analysis

TLDR
In order to clarify the potential of different clustering techniques in different application domains of microorganisms, related works from the 1990s till now are surveyed, while pinning out the specific challenges on each work (area) with the corresponding suitable clustering algorithm.

Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE)

TLDR
This poster presents a probabilistic procedure to quantify the immune response to coronavirus infection in mice and describes how these responses can be modelled and modified to improve care and save lives.

Severe acute respiratory syndrome-related coronavirus: The species and its viruses – a statement of the Coronavirus Study Group

TLDR
The Coronavirus Study Group (CSG) of the International Committee on Taxonomy of Viruses assessed the novelty of the human pathogen tentatively named 2019-nCoV and formally recognizes this virus as a sister to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Emerging novel coronavirus (2019-nCoV)—current scenario, evolutionary perspective based on genome analysis and recent developments

TLDR
The genetic analyses predict bats as the most probable source of 2019-nCoV though further investigations needed to confirm the origin of the novel virus, which has spread in 24 countries in a short span of time.

Molecular Diagnosis of a Novel Coronavirus (2019-nCoV) Causing an Outbreak of Pneumonia

TLDR
Two 1-step quantitative real-time reverse-transcription PCR assays can achieve a rapid detection of 2019n-CoV in human samples, thereby allowing early identification of patients.