pith:LFCUF6IJ
Cross Modality Image Translation In Medical Imaging Using Generative Frameworks
GANs outperform latent models in standardized 3D medical image translation across 11 oncology datasets.
arxiv:2605.13686 v1 · 2026-05-13 · cs.CV · cs.AI
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Claims
GANs outperform latent generative models across all tasks, with SRGAN achieving statistically significant superiority. Our lesion-level analysis reveals that all models struggle with small lesions and that, in CT to PET synthesis, models reproduce lesion shape more reliably than absolute uptake-related intensity. Visual Turing test shows near-chance classification accuracy (56.7%).
That uniform preprocessing, splitting, and inference rules across heterogeneous datasets and modalities do not inadvertently favor GAN architectures over latent models, and that the chosen eleven datasets adequately represent clinical variability in lesion size and contrast.
A uniform benchmark across 77 experiments finds SRGAN superior to latent diffusion models for 3D medical image translation, with synthetic volumes indistinguishable from real ones in a 17-physician Turing test.
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| First computed | 2026-05-18T02:44:16.993531Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LFCUF6IJ3CZWIKXE5VGH3FUBCL \
| jq -c '.canonical_record' \
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# expect: 594542f909d8b3642ae4ed4c7d968112e8c12dfdf89bc2f96d95124df5abb431
Canonical record JSON
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