MIRAGE: Multi-scale Lesion-Informed Representation with Auxiliary Guidance for MRI Contrast Enhancement

2026-07-21 · Source: Takara TLDR - Daily AI Papers · Field: Health & Wellbeing — Medical Specialties & Subspecialties, Medical Devices & Health Technology · Depth: Expert, quick

Summary

MIRAGE, a residual 2D U-Net, addresses the underdetermined problem of inferring contrast enhancement from pre-contrast breast MRI slices, where existing methods often suppress uncertain lesion enhancement or prioritize realistic appearance over patient-specific lesion fidelity. This model combines global reconstruction and perceptual losses with three lesion-aware supervisions available during training: an asymmetric penalty for missed tumor enhancement, multi-scale auxiliary tumor segmentation, and guidance via a frozen post-contrast tumor segmentation nnU-Net. Evaluated on 301 cases from the multi-centre MAMA-SYNTH data using eight complementary metrics, MIRAGE ranks first on six metrics and significantly improves downstream lesion localization compared to tuned pix2pix, conditional diffusion, and latent bridge-matching baselines. While generative alternatives retain advantages in LPIPS or contrast classification, revealing a fidelity-utility trade-off, ablations show losses are partly redundant for localization but distinct for appearance, radiomics, and boundary accuracy, supporting task-aware synthesis.

Key takeaway

For AI Scientists developing MRI contrast enhancement models or lesion detection systems, you should evaluate task-aware synthesis methods like MIRAGE. This approach offers superior lesion localization compared to purely generative models, which often prioritize realism over specific lesion fidelity. When designing your models, you must carefully define your utility metrics, as the apparent optimality of such methods is conditional on the downstream models and metrics used to define success.

Key insights

MIRAGE enhances MRI contrast inference by integrating lesion-aware supervision with global reconstruction, improving tumor localization.

Principles

• Optimizing pixel fidelity alone can suppress uncertain lesion enhancement.
• Generative models may prioritize realism over patient-specific lesion fidelity.
• Task-aware synthesis optimality depends on downstream models and metrics.

Method

MIRAGE is a residual 2D U-Net combining global reconstruction and perceptual losses with asymmetric tumor enhancement penalties, multi-scale auxiliary tumor segmentation, and guidance from a frozen post-contrast tumor segmentation nnU-Net during training.

In practice

• Use asymmetric penalties for critical feature enhancement.
• Integrate multi-scale auxiliary tasks for robust feature learning.
• Employ frozen expert models for training guidance.

Topics

• MRI Contrast Enhancement
• Breast MRI
• Lesion Segmentation
• U-Net Architecture
• Deep Learning
• Medical Imaging

Best for: AI Scientist, Research Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.