SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments Article Swipe
Gao Cong
,
Benjamin D. Killeen
,
HU Yi-cheng
,
Robert B. Grupp
,
Russell H. Taylor
,
Mehran Armand
,
Mathias Unberath
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2206.06127
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2206.06127
Artificial intelligence (AI) now enables automated interpretation of medical images for clinical use. However, AI's potential use for interventional images (versus those involved in triage or diagnosis), such as for guidance during surgery, remains largely untapped. This is because surgical AI systems are currently trained using post hoc analysis of data collected during live surgeries, which has fundamental and practical limitations, including ethical considerations, expense, scalability, data integrity, and a lack of ground truth. Here, we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection. We show that training AI image analysis models on realistically synthesized data, combined with contemporary domain generalization or adaptation techniques, results in models that on real data perform comparably to models trained on a precisely matched real data training set. Because synthetic generation of training data from human-based models scales easily, we find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real data-trained models due to the effectiveness of training on a larger dataset. We demonstrate the potential of SyntheX on three clinical tasks: Hip image analysis, surgical robotic tool detection, and COVID-19 lung lesion segmentation. SyntheX provides an opportunity to drastically accelerate the conception, design, and evaluation of intelligent systems for X-ray-based medicine. In addition, simulated image environments provide the opportunity to test novel instrumentation, design complementary surgical approaches, and envision novel techniques that improve outcomes, save time, or mitigate human error, freed from the ethical and practical considerations of live human data collection.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2206.06127
- https://arxiv.org/pdf/2206.06127
- OA Status
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- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4320234189Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2206.06127Digital Object Identifier
- Title
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SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico ExperimentsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-06-13Full publication date if available
- Authors
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Gao Cong, Benjamin D. Killeen, HU Yi-cheng, Robert B. Grupp, Russell H. Taylor, Mehran Armand, Mathias UnberathList of authors in order
- Landing page
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https://arxiv.org/abs/2206.06127Publisher landing page
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https://arxiv.org/pdf/2206.06127Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2206.06127Direct OA link when available
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Computer science, Artificial intelligence, Ground truth, Scalability, Machine learning, Segmentation, Workflow, Generalization, Test set, Data science, Mathematical analysis, Mathematics, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 1, 2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.systems | 41, 217 |
| abstract_inverted_index.trained | 44, 128 |
| abstract_inverted_index.COVID-19 | 199 |
| abstract_inverted_index.However, | 13 |
| abstract_inverted_index.SyntheX, | 164 |
| abstract_inverted_index.analysis | 48, 103 |
| abstract_inverted_index.clinical | 11, 189 |
| abstract_inverted_index.combined | 109 |
| abstract_inverted_index.creating | 78 |
| abstract_inverted_index.dataset. | 180 |
| abstract_inverted_index.envision | 238 |
| abstract_inverted_index.expense, | 64 |
| abstract_inverted_index.guidance | 30 |
| abstract_inverted_index.involved | 22 |
| abstract_inverted_index.mitigate | 247 |
| abstract_inverted_index.paradigm | 154 |
| abstract_inverted_index.provides | 204 |
| abstract_inverted_index.surgery, | 32 |
| abstract_inverted_index.surgical | 39, 194, 235 |
| abstract_inverted_index.training | 100, 135, 141, 176 |
| abstract_inverted_index.transfer | 153 |
| abstract_inverted_index.addition, | 222 |
| abstract_inverted_index.analysis, | 158, 193 |
| abstract_inverted_index.automated | 5 |
| abstract_inverted_index.collected | 51 |
| abstract_inverted_index.currently | 43 |
| abstract_inverted_index.including | 61 |
| abstract_inverted_index.medicine. | 220 |
| abstract_inverted_index.outcomes, | 243 |
| abstract_inverted_index.potential | 15, 184 |
| abstract_inverted_index.practical | 59, 255 |
| abstract_inverted_index.precisely | 131 |
| abstract_inverted_index.realistic | 79 |
| abstract_inverted_index.simulated | 80, 223 |
| abstract_inverted_index.synthetic | 138 |
| abstract_inverted_index.untapped. | 35 |
| abstract_inverted_index.Artificial | 0 |
| abstract_inverted_index.accelerate | 209 |
| abstract_inverted_index.adaptation | 115 |
| abstract_inverted_index.comparably | 125 |
| abstract_inverted_index.complement | 90 |
| abstract_inverted_index.detection, | 197 |
| abstract_inverted_index.evaluation | 214 |
| abstract_inverted_index.generation | 139 |
| abstract_inverted_index.integrity, | 67 |
| abstract_inverted_index.outperform | 167 |
| abstract_inverted_index.surgeries, | 54 |
| abstract_inverted_index.techniques | 240 |
| abstract_inverted_index.X-ray-based | 219 |
| abstract_inverted_index.alternative | 88 |
| abstract_inverted_index.approaches, | 236 |
| abstract_inverted_index.collection. | 96, 261 |
| abstract_inverted_index.conception, | 211 |
| abstract_inverted_index.demonstrate | 76, 182 |
| abstract_inverted_index.diagnosis), | 26 |
| abstract_inverted_index.drastically | 208 |
| abstract_inverted_index.fundamental | 57 |
| abstract_inverted_index.human-based | 144 |
| abstract_inverted_index.intelligent | 216 |
| abstract_inverted_index.large-scale | 92 |
| abstract_inverted_index.opportunity | 206, 228 |
| abstract_inverted_index.synthesized | 107 |
| abstract_inverted_index.techniques, | 116 |
| abstract_inverted_index.contemporary | 111 |
| abstract_inverted_index.data-trained | 169 |
| abstract_inverted_index.environments | 225 |
| abstract_inverted_index.intelligence | 1 |
| abstract_inverted_index.limitations, | 60 |
| abstract_inverted_index.scalability, | 65 |
| abstract_inverted_index.complementary | 234 |
| abstract_inverted_index.effectiveness | 174 |
| abstract_inverted_index.realistically | 106 |
| abstract_inverted_index.segmentation. | 202 |
| abstract_inverted_index.considerations | 256 |
| abstract_inverted_index.generalization | 113 |
| abstract_inverted_index.interpretation | 6 |
| abstract_inverted_index.interventional | 18 |
| abstract_inverted_index.considerations, | 63 |
| abstract_inverted_index.instrumentation, | 232 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |