Composite Deep Network with Feature Weighting for Improved Delineation of COVID Infection in Lung CT Article Swipe
Pallabi Dutta
,
Sushmita Mitra
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.01.17.23284673
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.01.17.23284673
An early effective screening and grading of COVID-19 has become imperative towards optimizing the limited available resources of the medical facilities. An automated segmentation of the infected volumes in lung CT is expected to significantly aid in the diagnosis and care of patients. However, an accurate demarcation of lesions remains problematic due to their irregular structure and location(s) within the lung. A novel deep learning architecture, Composite Deep network with Feature Weighting (CDNetFW) , is proposed for efficient delineation of infected regions from lung CT images. Initially a coarser-segmentation is performed directly at shallower levels, thereby facilitating discovery of robust and discriminatory characteristics in the hidden layers. The novel feature weighting module helps prioritise relevant feature maps to be probed, along with those regions containing crucial information within these maps. This is followed by estimating the severity of the disease. The deep network CDNetFW has been shown to outperform several state-of-the-art architectures in the COVID-19 lesion segmentation task, as measured by experimental results on CT slices from publicly available datasets, especially when it comes to defining structures involving complex geometries.
Related Topics
Concepts
Weighting
Segmentation
Artificial intelligence
Feature (linguistics)
Computer science
Deep learning
Coronavirus disease 2019 (COVID-19)
Pattern recognition (psychology)
Convolutional neural network
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Radiology
Medicine
Infectious disease (medical specialty)
Disease
Pathology
Linguistics
Philosophy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.01.17.23284673
- https://www.medrxiv.org/content/medrxiv/early/2023/02/17/2023.01.17.23284673.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4317387903
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4317387903Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.01.17.23284673Digital Object Identifier
- Title
-
Composite Deep Network with Feature Weighting for Improved Delineation of COVID Infection in Lung CTWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-18Full publication date if available
- Authors
-
Pallabi Dutta, Sushmita MitraList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.01.17.23284673Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/02/17/2023.01.17.23284673.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/02/17/2023.01.17.23284673.full.pdfDirect OA link when available
- Concepts
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Weighting, Segmentation, Artificial intelligence, Feature (linguistics), Computer science, Deep learning, Coronavirus disease 2019 (COVID-19), Pattern recognition (psychology), Convolutional neural network, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Radiology, Medicine, Infectious disease (medical specialty), Disease, Pathology, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 2Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.early | 2 |
| abstract_inverted_index.helps | 113 |
| abstract_inverted_index.lung. | 61 |
| abstract_inverted_index.maps. | 130 |
| abstract_inverted_index.novel | 63, 109 |
| abstract_inverted_index.shown | 147 |
| abstract_inverted_index.task, | 158 |
| abstract_inverted_index.their | 54 |
| abstract_inverted_index.these | 129 |
| abstract_inverted_index.those | 123 |
| abstract_inverted_index.become | 10 |
| abstract_inverted_index.hidden | 106 |
| abstract_inverted_index.lesion | 156 |
| abstract_inverted_index.module | 112 |
| abstract_inverted_index.robust | 100 |
| abstract_inverted_index.slices | 166 |
| abstract_inverted_index.within | 59, 128 |
| abstract_inverted_index.CDNetFW | 144 |
| abstract_inverted_index.Feature | 71 |
| abstract_inverted_index.complex | 179 |
| abstract_inverted_index.crucial | 126 |
| abstract_inverted_index.feature | 110, 116 |
| abstract_inverted_index.grading | 6 |
| abstract_inverted_index.images. | 86 |
| abstract_inverted_index.layers. | 107 |
| abstract_inverted_index.lesions | 49 |
| abstract_inverted_index.levels, | 95 |
| abstract_inverted_index.limited | 15 |
| abstract_inverted_index.medical | 20 |
| abstract_inverted_index.network | 69, 143 |
| abstract_inverted_index.probed, | 120 |
| abstract_inverted_index.regions | 82, 124 |
| abstract_inverted_index.remains | 50 |
| abstract_inverted_index.results | 163 |
| abstract_inverted_index.several | 150 |
| abstract_inverted_index.thereby | 96 |
| abstract_inverted_index.towards | 12 |
| abstract_inverted_index.volumes | 28 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.COVID-19 | 8, 155 |
| abstract_inverted_index.However, | 44 |
| abstract_inverted_index.accurate | 46 |
| abstract_inverted_index.defining | 176 |
| abstract_inverted_index.directly | 92 |
| abstract_inverted_index.disease. | 140 |
| abstract_inverted_index.expected | 33 |
| abstract_inverted_index.followed | 133 |
| abstract_inverted_index.infected | 27, 81 |
| abstract_inverted_index.learning | 65 |
| abstract_inverted_index.measured | 160 |
| abstract_inverted_index.proposed | 76 |
| abstract_inverted_index.publicly | 168 |
| abstract_inverted_index.relevant | 115 |
| abstract_inverted_index.severity | 137 |
| abstract_inverted_index.(CDNetFW) | 73 |
| abstract_inverted_index.Composite | 67 |
| abstract_inverted_index.Initially | 87 |
| abstract_inverted_index.Weighting | 72 |
| abstract_inverted_index.automated | 23 |
| abstract_inverted_index.available | 16, 169 |
| abstract_inverted_index.datasets, | 170 |
| abstract_inverted_index.diagnosis | 39 |
| abstract_inverted_index.discovery | 98 |
| abstract_inverted_index.effective | 3 |
| abstract_inverted_index.efficient | 78 |
| abstract_inverted_index.involving | 178 |
| abstract_inverted_index.irregular | 55 |
| abstract_inverted_index.patients. | 43 |
| abstract_inverted_index.performed | 91 |
| abstract_inverted_index.resources | 17 |
| abstract_inverted_index.screening | 4 |
| abstract_inverted_index.shallower | 94 |
| abstract_inverted_index.structure | 56 |
| abstract_inverted_index.weighting | 111 |
| abstract_inverted_index.containing | 125 |
| abstract_inverted_index.especially | 171 |
| abstract_inverted_index.estimating | 135 |
| abstract_inverted_index.imperative | 11 |
| abstract_inverted_index.optimizing | 13 |
| abstract_inverted_index.outperform | 149 |
| abstract_inverted_index.prioritise | 114 |
| abstract_inverted_index.structures | 177 |
| abstract_inverted_index.delineation | 79 |
| abstract_inverted_index.demarcation | 47 |
| abstract_inverted_index.facilities. | 21 |
| abstract_inverted_index.geometries. | 180 |
| abstract_inverted_index.information | 127 |
| abstract_inverted_index.location(s) | 58 |
| abstract_inverted_index.problematic | 51 |
| abstract_inverted_index.experimental | 162 |
| abstract_inverted_index.facilitating | 97 |
| abstract_inverted_index.segmentation | 24, 157 |
| abstract_inverted_index.architecture, | 66 |
| abstract_inverted_index.architectures | 152 |
| abstract_inverted_index.significantly | 35 |
| abstract_inverted_index.discriminatory | 102 |
| abstract_inverted_index.characteristics | 103 |
| abstract_inverted_index.state-of-the-art | 151 |
| abstract_inverted_index.coarser-segmentation | 89 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5006358215 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I6498739 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6899999976158142 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.65222233 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |