Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data Article Swipe
H. Das
,
Ryan Tran
,
Japjot Singh
,
Xiangyu Yue
,
Geoffrey H. Tison
,
Alberto Sangiovanni‐Vincentelli
,
Costas J. Spanos
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v36i11.21435
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v36i11.21435
Background: At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine Learning (ML) models trained with the available data, which is limited in quantity and poor in diversity, will often be biased and inaccurate. At the same time, ML algorithms designed to fight pandemics must have good performance and be developed in a time-sensitive manner. To tackle the challenges of limited data, and label scarcity in the available data, we propose generating conditional synthetic data, to be used alongside real data for developing robust ML models. Methods: We present a hybrid model consisting of a conditional generative flow and a classifier for conditional synthetic data generation. The classifier decouples the feature representation for the condition, which is fed to the flow to extract the local noise. We generate synthetic data by manipulating the local noise with fixed conditional feature representation. We also propose a semi-supervised approach to generate synthetic samples in the absence of labels for a majority of the available data. Results: We performed conditional synthetic generation for chest computed tomography (CT) scans corresponding to normal, COVID-19, and pneumonia afflicted patients. We show that our method significantly outperforms existing models both on qualitative and quantitative performance, and our semi-supervised approach can efficiently synthesize conditional samples under label scarcity. As an example of downstream use of synthetic data, we show improvement in COVID-19 detection from CT scans with conditional synthetic data augmentation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v36i11.21435
- https://ojs.aaai.org/index.php/AAAI/article/download/21435/21184
- OA Status
- diamond
- Cited By
- 37
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3201185848
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3201185848Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v36i11.21435Digital Object Identifier
- Title
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Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic DataWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-06-28Full publication date if available
- Authors
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H. Das, Ryan Tran, Japjot Singh, Xiangyu Yue, Geoffrey H. Tison, Alberto Sangiovanni‐Vincentelli, Costas J. SpanosList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v36i11.21435Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/21435/21184Direct link to full text PDF
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/21435/21184Direct OA link when available
- Concepts
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Synthetic data, Computer science, Machine learning, Artificial intelligence, Classifier (UML), Pattern recognition (psychology), Feature (linguistics), Data mining, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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37Total citation count in OpenAlex
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2025: 8, 2024: 7, 2023: 15, 2022: 6, 2021: 1Per-year citation counts (last 5 years)
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58Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2022-06-28 |
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| referenced_works | https://openalex.org/W2978534880, https://openalex.org/W6799183208, https://openalex.org/W4288009332, https://openalex.org/W3133293092, https://openalex.org/W3045644133, https://openalex.org/W2909860570, https://openalex.org/W2940898493, https://openalex.org/W6765873326, https://openalex.org/W3126873962, https://openalex.org/W3034988872, https://openalex.org/W3133482524, https://openalex.org/W3127355612, https://openalex.org/W2770645414, https://openalex.org/W3005304986, https://openalex.org/W3024801014, https://openalex.org/W6775188128, https://openalex.org/W2556897635, https://openalex.org/W2966927162, https://openalex.org/W6757794741, https://openalex.org/W2330219538, https://openalex.org/W2964972896, https://openalex.org/W4289761690, https://openalex.org/W2963139417, https://openalex.org/W2912298597, https://openalex.org/W2409550820, https://openalex.org/W3105081694, https://openalex.org/W2097117768, https://openalex.org/W3110862931, https://openalex.org/W4297798428, https://openalex.org/W2989560243, https://openalex.org/W3107948185, https://openalex.org/W3008296058, https://openalex.org/W3037964252, https://openalex.org/W1959608418, https://openalex.org/W3195226045, https://openalex.org/W3182322007, https://openalex.org/W2176412452, https://openalex.org/W3129312489, https://openalex.org/W2187089797, https://openalex.org/W2921938492, https://openalex.org/W3118284102, https://openalex.org/W3137944646, https://openalex.org/W2963777311, https://openalex.org/W3201409833, https://openalex.org/W2963373786, https://openalex.org/W3093712558, https://openalex.org/W2966459487, https://openalex.org/W3161175155, https://openalex.org/W2905589284, https://openalex.org/W2990489240, https://openalex.org/W2962212997, https://openalex.org/W3114166611, https://openalex.org/W3187503080, https://openalex.org/W2963233928, https://openalex.org/W2980809349, https://openalex.org/W2767699072, https://openalex.org/W3035009331, https://openalex.org/W3020275213 |
| referenced_works_count | 58 |
| abstract_inverted_index.a | 5, 66, 103, 108, 113, 157, 170 |
| abstract_inverted_index.As | 223 |
| abstract_inverted_index.At | 1, 48 |
| abstract_inverted_index.CT | 239 |
| abstract_inverted_index.ML | 52, 98 |
| abstract_inverted_index.To | 69 |
| abstract_inverted_index.We | 101, 140, 154, 177, 196 |
| abstract_inverted_index.an | 224 |
| abstract_inverted_index.as | 8 |
| abstract_inverted_index.be | 20, 44, 63, 90 |
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| abstract_inverted_index.is | 34, 130 |
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| abstract_inverted_index.we | 83, 232 |
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| abstract_inverted_index.and | 38, 46, 62, 76, 112, 192, 208, 211 |
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| abstract_inverted_index.our | 199, 212 |
| abstract_inverted_index.the | 2, 16, 30, 49, 71, 80, 123, 127, 133, 137, 146, 165, 173 |
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| abstract_inverted_index.(ML) | 26 |
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| abstract_inverted_index.chest | 183 |
| abstract_inverted_index.data, | 32, 75, 82, 88, 231 |
| abstract_inverted_index.data. | 175 |
| abstract_inverted_index.fight | 56 |
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| abstract_inverted_index.local | 138, 147 |
| abstract_inverted_index.might | 19 |
| abstract_inverted_index.model | 105 |
| abstract_inverted_index.noise | 148 |
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| abstract_inverted_index.scans | 187, 240 |
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| abstract_inverted_index.models | 27, 204 |
| abstract_inverted_index.noise. | 139 |
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| abstract_inverted_index.tackle | 70 |
| abstract_inverted_index.Machine | 24 |
| abstract_inverted_index.absence | 166 |
| abstract_inverted_index.disease | 18 |
| abstract_inverted_index.example | 225 |
| abstract_inverted_index.extract | 136 |
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| abstract_inverted_index.manner. | 68 |
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| abstract_inverted_index.normal, | 190 |
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| abstract_inverted_index.propose | 84, 156 |
| abstract_inverted_index.samples | 163, 219 |
| abstract_inverted_index.sparse. | 23 |
| abstract_inverted_index.trained | 28 |
| abstract_inverted_index.COVID-19 | 236 |
| abstract_inverted_index.Learning | 25 |
| abstract_inverted_index.Methods: | 100 |
| abstract_inverted_index.Results: | 176 |
| abstract_inverted_index.approach | 159, 214 |
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| abstract_inverted_index.COVID-19, | 9, 191 |
| abstract_inverted_index.afflicted | 194 |
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| abstract_inverted_index.decouples | 122 |
| abstract_inverted_index.detection | 237 |
| abstract_inverted_index.developed | 64 |
| abstract_inverted_index.pandemic, | 6 |
| abstract_inverted_index.pandemics | 57 |
| abstract_inverted_index.patients. | 195 |
| abstract_inverted_index.performed | 178 |
| abstract_inverted_index.pneumonia | 193 |
| abstract_inverted_index.scarcity. | 222 |
| abstract_inverted_index.synthetic | 87, 117, 142, 162, 180, 230, 243 |
| abstract_inverted_index.algorithms | 53 |
| abstract_inverted_index.challenges | 72 |
| abstract_inverted_index.classifier | 114, 121 |
| abstract_inverted_index.condition, | 128 |
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| abstract_inverted_index.developing | 96 |
| abstract_inverted_index.diversity, | 41 |
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| abstract_inverted_index.generative | 110 |
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| abstract_inverted_index.tomography | 185 |
| abstract_inverted_index.Background: | 0 |
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| abstract_inverted_index.efficiently | 216 |
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| abstract_inverted_index.improvement | 234 |
| abstract_inverted_index.inaccurate. | 47 |
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| abstract_inverted_index.augmentation. | 245 |
| abstract_inverted_index.corresponding | 14, 188 |
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| abstract_inverted_index.time-sensitive | 67 |
| abstract_inverted_index.representation. | 153 |
| abstract_inverted_index.semi-supervised | 158, 213 |
| abstract_inverted_index.labeling/attributes | 13 |
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| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.99231117 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |