Mutual Distillation of Dual-Foundation Models for Semi-Supervised PET/CT Segmentation
Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology. To ease the high annotation cost of PET/CT segmentation, semi-supervised learning (SSL) provides a practical and effective solution for developing deep models with limited labeled data. Recent developments in visual foundation models have demonstrated remarkable adaptability with improved efficiency. In this work, we propose a mutual distillation framework that seamlessly exploits both structural and functional foundation models, which act as modality-specific generalists for distilling knowledge from structural CT and metabolic PET imaging. By bridging the gap between the task-spec...
| Authors | Fuyou Mao, Beining Wu, Yanfeng Jiang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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