Research map / Label-efficient and robust learning / Self-supervised and contrastive
Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add self-supervised self-representation network (ablation) | Auto-GAN · BraTS T2 synthesis | SSIM: 0.8800 -> 0.9157 | Helped |
| add self-supervised self-representation network (ablation) | Auto-GAN · BraTS T2 synthesis | FSIM: 0.9498 -> 0.9592 | Helped |
| use more input modalities (collaborative encoder branches 1 to 3) (ablation) | Auto-GAN · BraTS T2 synthesis | SSIM/FSIM | Helped |
| Auto-GAN vs pix2pix, CycleGAN, StarGAN | T1 to CT translation · ADNI | FID: 129.61 -> 64.29 | Helped |
| Auto-GAN vs best baseline | T1 to CT translation · ADNI | IS: 1.37 -> 2.15 | Helped |
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