Beyond Data Scarcity Optimizing R3GAN for Medical Image Generation from Small Datasets
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911256971051008 |
|---|---|
| author | Pan, Tsung-Wei Wu, Chang-Hong Wang, Jung-Hua Chen, Ming-Jer Yi, Yu-Chiao Lee, Tsung-Hsien |
| author_facet | Pan, Tsung-Wei Wu, Chang-Hong Wang, Jung-Hua Chen, Ming-Jer Yi, Yu-Chiao Lee, Tsung-Hsien |
| contents | Medical image datasets frequently exhibit significant class imbalance, a challenge that is further amplified by the inherently limited sample sizes that characterize clinical imaging data. Using human embryo time-lapse imaging (TLI) as a case study, this work investigates how generative adversarial networks (GANs) can be optimized for small datasets to generate realistic and diagnostically meaningful images. Based on systematic experiments with R3GAN, we established effective training strategies and designed an optimized configuration for 256x256-resolution datasets, featuring a full burn-in phase and a low, gradually increasing gamma range (5 to 40). The generated samples were used to balance an imbalanced embryo dataset, leading to substantial improvement in classification performance. The recall and F1-score of the three-cell (t3) class increased from 0.06 to 0.69 and from 0.11 to 0.60, respectively, without compromising the performance of other classes. These results demonstrate that tailored R3GAN training strategies can effectively alleviate data scarcity and improve model robustness in small-scale medical imaging tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26828 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Data Scarcity Optimizing R3GAN for Medical Image Generation from Small Datasets Pan, Tsung-Wei Wu, Chang-Hong Wang, Jung-Hua Chen, Ming-Jer Yi, Yu-Chiao Lee, Tsung-Hsien Image and Video Processing Artificial Intelligence Medical image datasets frequently exhibit significant class imbalance, a challenge that is further amplified by the inherently limited sample sizes that characterize clinical imaging data. Using human embryo time-lapse imaging (TLI) as a case study, this work investigates how generative adversarial networks (GANs) can be optimized for small datasets to generate realistic and diagnostically meaningful images. Based on systematic experiments with R3GAN, we established effective training strategies and designed an optimized configuration for 256x256-resolution datasets, featuring a full burn-in phase and a low, gradually increasing gamma range (5 to 40). The generated samples were used to balance an imbalanced embryo dataset, leading to substantial improvement in classification performance. The recall and F1-score of the three-cell (t3) class increased from 0.06 to 0.69 and from 0.11 to 0.60, respectively, without compromising the performance of other classes. These results demonstrate that tailored R3GAN training strategies can effectively alleviate data scarcity and improve model robustness in small-scale medical imaging tasks. |
| title | Beyond Data Scarcity Optimizing R3GAN for Medical Image Generation from Small Datasets |
| topic | Image and Video Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2510.26828 |