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ARXIV:2605.13686 · MEDICAL IMAGING AI · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13686MEDICAL IMAGING AISUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHGiulia Romoli · Alessia Capoccia · Filippo Ruffini · Francesco Di Feola · Luca Boldrini · Arturo Chiti · +17 at arXiv
A standardized framework for 3D medical image translation shows SRGAN outperforms latent generative models, with synthetic images proving indistinguishable from real ones to physicians.
Opportunity summary
Pain A standardized framework for 3D medical image translation shows SRGAN outperforms latent generative models, with synthetic images proving indistinguishable from real ones to physicians.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A standardized framework for 3D medical image translation shows SRGAN outperforms latent generative models, with synthetic images proving indistinguishable from real ones to physicians. the synthesis of a target imaging modality from a source…
Medical image-to-image (I2I) translation enables virtual scanning, i.e. the synthesis of a target imaging modality from a source one without additional acquisitions.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Medical image-to-image (I2I) translation enables virtual scanning, i.e. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Medical Imaging AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A standardized framework for 3D medical image translation shows SRGAN outperforms latent generative models, with synthetic images proving indistinguishable from real ones to physicians.
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Paper Pack
10.48550/arXiv.2605.13686A standardized framework for 3D medical image translation shows SRGAN outperforms latent generative models, with synthetic images proving indistinguishable from real ones to physicians.
Abstract
Medical image-to-image (I2I) translation enables virtual scanning, i.e. the synthesis of a target imaging modality from a source one without additional acquisitions. Despite growing interest, most proposed methods operate on 2D slices, are evaluated on isolated tasks with different experimental set-ups and lack clinical validation. The primary contribution of this work is a reproducible, standardized comparative evaluation of 3D I2I translation methods in oncological imaging, designed to standardize preprocessing, splitting, inference, and multi-level evaluation across heterogeneous clinical tasks. Within this framework, we compare seven generative models, three Generative Adversarial Networks (GANs: Pix2Pix, CycleGAN, SRGAN) and four latent generative models (Latent Diffusion Model, Latent Diffusion Model+ControlNet, Brownian Bridge, Flow Matching), across eleven datasets spanning three anatomical regions (head/neck, lung, pelvis) and four translation directions (cone-beam CT to CT, MRI to CT, CT to PET, MRI T2-weighted to T2-FLAIR), for a total of 77 experiments under uniform training, inference, and evaluation conditions. The results show that 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. We also performed a Visual Turing test administered to 17 physicians, including 15 radiologists, which shows near-chance classification accuracy (56.7%), confirming that synthetic volumes are largely indistinguishable from real acquisitions, while exposing a dissociation between quantitative metrics and clinical preference.
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Dimensions overall score 7.0
PROBLEM
A standardized framework for 3D medical image translation shows SRGAN outperforms latent generative models, with synthetic images proving indistinguishable from real ones to physicians. the synthesis of a target imaging modality from a source one without additional acquisitions.
METHOD
Medical image-to-image (I2I) translation enables virtual scanning, i.e. the synthesis of a target imaging modality from a source one without additional acquisitions.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Medical image-to-image (I2I) translation enables virtual scanning, i.e. Code availability is flagged in the production record; the public repository link still needs proof alignment.
WHY NOW
Medical Imaging AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A standardized framework for 3D medical image translation shows SRGAN outperforms latent generative models, with synthetic images proving indistinguishable from real ones to physicians. the synthesis of a target imaging modality from a source one without additional acquisitions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical image-to-image (I2I) translation enables virtual scanning, i.e. the synthesis of a target imaging modality from a source one without additional acquisitions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Medical image-to-image (I2I) translation enables virtual scanning, i.e. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical Imaging AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A standardized framework for 3D medical image translation shows SRGAN outperforms latent generative models, with synthetic images proving indistinguishable from real ones to physicians.
Segment
Medical Imaging AI
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Commercial read
7.0/10 public viability
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Technical feasibility
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