Shanghai Artificial Intelligence Laboratory

The work proposes BiasRecon, a bias-calibrated adaptation framework grounded in a minimal-intervention principle, which alternates frequency-guided prior calibration (learnable scalars α and β modulating low- and high-frequency components at each U-Net skip layer, adding only 2L parameters), score-based denoising, and adaptive regularization that uses Stein's Unbiased Risk Estimator (SURE) to tune the regularization parameter γ; trained on FastMRI knee (973 volumes) and evaluated without retraining on FastMRI-Brain (cross-anatomy), Stanford Knee (cross-center) and CMRxRecon (combined shift) under 8× Gaussian 1D undersampling, it improves PSNR over DDS by +1.19 dB, +2.06 dB and +1.60 dB respectively, and is best in 10 of 12 sampling/acceleration settings.

The work proposes BiasRecon, a bias-calibrated adaptation framework grounded in a minimal-intervention principle, which alternates frequency-guided prior calibration (learnable scalars α and β modulating low- and high-frequency components at each U-Net skip layer, adding only 2L parameters), score-based denoising, and adaptive regularization that uses Stein's Unbiased Risk Estimator (SURE) to tune the regularization parameter γ; trained on FastMRI knee (973 volumes) and evaluated without retraining on FastMRI-Brain (cross-anatomy), Stanford Knee (cross-center) and CMRxRecon (combined shift) under 8× Gaussian 1D undersampling, it improves PSNR over DDS by +1.19 dB, +2.06 dB and +1.60 dB respectively, and is best in 10 of 12 sampling/acceleration settings.

The work proposes MedQ-Engine, a closed-loop data engine that iterates through evaluate-explore-evolve phases: it clusters model failures on a development set into failure prototypes, uses the visual component of those prototypes as retrieval anchors over a roughly one-million-image pool spanning five modalities, and combines progressive human-in-the-loop annotation with entropy-guided routing and quality-assured fine-tuning, so that with only 10K annotations an 8B-parameter model surpasses GPT-4o by over 13% on medical image quality assessment and narrows the gap to human experts to 4.34%, with more than 4x sample efficiency over random sampling.

The work proposes MedQ-Engine, a closed-loop data engine that iterates through evaluate-explore-evolve phases: it clusters model failures on a development set into failure prototypes, uses the visual component of those prototypes as retrieval anchors over a roughly one-million-image pool spanning five modalities, and combines progressive human-in-the-loop annotation with entropy-guided routing and quality-assured fine-tuning, so that with only 10K annotations an 8B-parameter model surpasses GPT-4o by over 13% on medical image quality assessment and narrows the gap to human experts to 4.34%, with more than 4x sample efficiency over random sampling.