Synthetic Data in Radiological Imaging: Current State and Future Outlook
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917709894123520 |
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| author | Sizikova, Elena Badal, Andreu Delfino, Jana G. Lago, Miguel Nelson, Brandon Saharkhiz, Niloufar Sahiner, Berkman Zamzmi, Ghada Badano, Aldo |
| author_facet | Sizikova, Elena Badal, Andreu Delfino, Jana G. Lago, Miguel Nelson, Brandon Saharkhiz, Niloufar Sahiner, Berkman Zamzmi, Ghada Badano, Aldo |
| contents | A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdensome due to high acquisition cost, safety limitations, patient privacy restrictions or low disease prevalence rates. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances. We summarize key research trends and practical uses for synthetically generated data for radiological applications of AI. Specifically, we discuss different types of techniques for generating synthetic examples, their main application areas, and related quality control assessment issues. We also discuss current approaches for evaluating synthetic imaging data. Overall, synthetic data holds great promise in addressing current data availability gaps, but additional work is needed before its full potential is realized. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01561 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synthetic Data in Radiological Imaging: Current State and Future Outlook Sizikova, Elena Badal, Andreu Delfino, Jana G. Lago, Miguel Nelson, Brandon Saharkhiz, Niloufar Sahiner, Berkman Zamzmi, Ghada Badano, Aldo Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdensome due to high acquisition cost, safety limitations, patient privacy restrictions or low disease prevalence rates. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances. We summarize key research trends and practical uses for synthetically generated data for radiological applications of AI. Specifically, we discuss different types of techniques for generating synthetic examples, their main application areas, and related quality control assessment issues. We also discuss current approaches for evaluating synthetic imaging data. Overall, synthetic data holds great promise in addressing current data availability gaps, but additional work is needed before its full potential is realized. |
| title | Synthetic Data in Radiological Imaging: Current State and Future Outlook |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.01561 |