Congratulations on CVPR 2026 Acceptance!

We are excited to announce that our paper has been accepted to CVPR 2026:

Yeongsu Kim, Seo-Yeon Choi, and Kyungsu Lee, “Human-Intervention Segmentation via Federated Intent Embedding and Multi-Mask Recommendation.”

• Venue: CVPR 2026 (Conference)
• Subject Area: Vision applications and systems
• Keywords: Computer Vision, Machine Learning, User Experience Design
• Student Paper: Yes

Abstract:

Artificial intelligence (AI) has advanced radiology, yet variability across hospitals and devices undermines reliability and trust. We present a federated learning framework that combines frequency-domain harmonization and instruction-conditioned personalization to deliver consistent and interpretable diagnostic outcomes. Using FFT-based reconstructions informed by radiomics descriptors, the system reduces equipment dependency, while CLIP-based text conditioning enables clinicians to guide reconstructions to local practices and patient needs. We evaluated the framework across four hospitals with fifteen radiologists and fifty patients, spanning polyp detection, rotator cuff tear diagnosis, pneumothorax classification, and breast cancer classification/segmentation. Results show significant gains in accuracy, calibration, and robustness under cross-site transfer, without introducing prohibitive latency. Radiologists reported improved interpretability and preserved professional agency, while patients expressed greater trust, reduced anxiety, and stronger acceptance of AI involvement. This work advances a human-centered design for medical AI, aligning federated learning with transparency, equity, and trustworthy deployment.

Congratulations to the authors on this excellent result.