Publication
Abdominal MRI synthesis using styleGAN2-ADA
| dc.contributor.author | Gonçalves, Bernardo | |
| dc.contributor.author | Vieira, Pedro | |
| dc.contributor.author | Vieira, Ana | |
| dc.date.accessioned | 2024-01-03T11:50:15Z | |
| dc.date.embargo | 2025-07-31 | |
| dc.date.issued | 2023-07 | |
| dc.description | This work was funded by FCT—Portuguese Foundation for Science and Technology and Bee2Fire SA under the PhD grant with reference PD/BDE/150624/2020. | pt_PT |
| dc.description.abstract | The lack of labeled medical data still poses one of the biggest issues when creating Deep Learning models in the medical field. Modern data augmentation techniques like the generation of synthetic images have gained a special interest. In recent years there has been a significant improvement in GANs. StyleGAN2 achieves impressive results in the generation of natural images. StyleGAN2-ADA was created to respond to the lack of training data when training an image synthesis model, which is very frequent in the medical field. Some works used styleGAN to generate melanomas, breast cancer histological images, and MR and CT images. In this work, we apply, for the first time, a styleGAN2-ADA to a small dataset of abdominal MRI with 1.3k images. From the augmentation pipeline created by the authors of styleGAN2-ADA, we removed all augmentations except the geometric transformations and pixel blitting operations. We trained our network for 70 hours. Our generated dataset has a precision score of 59,33 % and a FID score of 18,14. We conclude that the styleGAN2-ADA is a viable solution to generate MRI using a small dataset. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Gonçalves B, Vieira P, Vieira A (Ana). Abdominal MRI synthesis using styleGAN2-ADA. In: 2023 IST-Africa Conference (IST-Africa), Tshwane (South Africa), May 31 – June 02, 2023. | pt_PT |
| dc.identifier.doi | 10.23919/IST-Africa60249.2023.10187755 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.21/16750 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/10187755 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Magnetic resonance imaging | pt_PT |
| dc.subject | Generative adversarial networks | pt_PT |
| dc.subject | StyleGAN2 | pt_PT |
| dc.subject | Image synthesis | pt_PT |
| dc.subject | Medical imaging | pt_PT |
| dc.subject | Deep learning | pt_PT |
| dc.subject | Malignant tumour | pt_PT |
| dc.subject | Pipelines | pt_PT |
| dc.subject | Training data | pt_PT |
| dc.title | Abdominal MRI synthesis using styleGAN2-ADA | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 9 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | article | pt_PT |
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