Name
Title | ||
|---|---|---|
Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection. |
Abstract | ||
|---|---|---|
One drawback of Computer Aided Detection (CADe) systems is the large amount of data needed to train them, which may be expensive in the medical field. We propose using a generative adversarial network (GAN) as a potential data augmentation strategy to generate more training data to improve CADe. In our preliminary results, using the NIH/NCI Lung Image Database Consortium, we obtained a higher sensitivity when training a CADe system on our augmented lung nodule 3D data than training it without. We show that GANs are a viable method of data augmentation for lung nodule detection and are a promising area of potential research in the CADe domain. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/12.2513011 | Proceedings of SPIE |
Field | DocType | Volume |
Computer science,Artificial intelligence,Generative grammar,Machine learning,Adversarial system | Conference | 10950 |
ISSN | Citations | PageRank |
0277-786X | 2 | 0.42 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chufan Gao | 1 | 2 | 0.42 |
| Stephen Clark | 2 | 2 | 0.42 |
| Jacob D. Furst | 3 | 545 | 56.63 |
| Daniela Stan Raicu | 4 | 469 | 46.22 |