MIRAGE: Multi-scale Lesion-Informed Representation with Auxiliary Guidance for MRI Contrast Enhancement
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76
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90
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44
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Merged summary
TL;DR - MIRAGE synthesizes contrast-enhanced breast MRI from a pre-contrast slice using lesion-aware training supervision. It improves patient-specific lesion localization while exposing trade-offs between clinical utility and realistic image generation.
- Uses a residual 2D U-Net with reconstruction, perceptual, asymmetric enhancement, auxiliary segmentation, and frozen nnU-Net guidance losses.
- Ranks first on six of eight metrics across 301 multi-centre MAMA-SYNTH cases.
- Outperforms pix2pix, conditional diffusion, and latent bridge-matching baselines in downstream lesion localization.
- Generative baselines remain stronger on LPIPS or contrast classification, highlighting a fidelity–utility trade-off.
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MIRAGE: Multi-scale Lesion-Informed Representation with Auxiliary Guidance for MRI Contrast Enhancement
Public signals
Hugging Face upvotes 0
TL;DR - MIRAGE synthesizes contrast-enhanced breast MRI from a pre-contrast slice using lesion-aware training supervision. It improves patient-specific lesion localization while exposing trade-offs between clinical utility and realistic image generation.
- Uses a residual 2D U-Net with reconstruction, perceptual, asymmetric enhancement, auxiliary segmentation, and frozen nnU-Net guidance losses.
- Ranks first on six of eight metrics across 301 multi-centre MAMA-SYNTH cases.
- Outperforms pix2pix, conditional diffusion, and latent bridge-matching baselines in downstream lesion localization.
- Generative baselines remain stronger on LPIPS or contrast classification, highlighting a fidelity–utility trade-off.