Abstract | ||
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In the healthcare domain, datasets are often private and lack large amounts of samples, making it difficult to cope with the inherent patient data heterogeneity. As an attempt to mitigate data scarcity, generative models are being used due to their ability to produce new data, using a dataset as a reference. However, synthesis studies often rely on a 2D representation of data, a seriously limited form of information when it comes to lung computed tomography scans where, for example, pathologies like nodules can manifest anywhere in the organ. Here, we develop a 3D Progressive Growing Generative Adversarial Network capable of generating thoracic CT volumes at a resolution of 128, and analyze the model outputs through a quantitative metric (3D Muli-Scale Structural Similarity) and a Visual Turing Test. Clinical relevance - This paper is a novel application of the 3D PGGAN model to synthesize CT lung scans. This preliminary study focuses on synthesizing the entire volume of the lung rather than just the lung nodules. The synthesized data represent an attempt to mitigate data scarcity which is one of the major limitations to create learning models with good generalization in healthcare. |
Year | DOI | Venue |
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2022 | 10.1109/EMBC48229.2022.9871481 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
DocType | Volume | ISSN |
Conference | 2022 | 2694-0604 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Artur Ferreira | 1 | 0 | 0.34 |
Tania Pereira | 2 | 0 | 1.69 |
Francisco Silva | 3 | 0 | 2.03 |
Ana T Vilares | 4 | 0 | 0.34 |
Miguel C Silva | 5 | 0 | 0.34 |
Antonio Cunha | 6 | 0 | 0.68 |
Helder P Oliveira | 7 | 0 | 1.35 |