MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation
为解决PET-CT全身病变分割中数据标注稀缺和领域迁移问题，提出MUST-PET自监督学习框架，通过多模态、多示踪剂训练提升泛化性能。
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Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets
This work addresses the performance and generalization bottlenecks in whole-body lesion segmentation caused by scarce annotations, proposing FEEDS—a single-step, efficient training paradigm grounded in vision foundation model embeddings. FEEDS selects informative and diverse samples from large-scale unlabeled PET/CT data in a single pass for expert annotation, eliminating the need for iterative pseudo-labeling or active learning loops, thereby substantially reducing both annotation and computational costs. Using only 30% of the annotated data, the method achieves segmentation performance on par with fully supervised models across multiple datasets—including AutoPET-III, DeepPSMA, and an internal multicenter, multi-tracer cohort—demonstrating significantly superior generalization compared to random sampling and semi-supervised baselines.
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MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation
为解决PET-CT全身病变分割中数据标注稀缺和领域迁移问题，提出MUST-PET自监督学习框架，通过多模态、多示踪剂训练提升泛化性能。
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Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets
This work addresses the performance and generalization bottlenecks in whole-body lesion segmentation caused by scarce annotations, proposing FEEDS—a single-step, efficient training paradigm grounded in vision foundation model embeddings. FEEDS selects informative and diverse samples from large-scale unlabeled PET/CT data in a single pass for expert annotation, eliminating the need for iterative pseudo-labeling or active learning loops, thereby substantially reducing both annotation and computational costs. Using only 30% of the annotated data, the method achieves segmentation performance on par with fully supervised models across multiple datasets—including AutoPET-III, DeepPSMA, and an internal multicenter, multi-tracer cohort—demonstrating significantly superior generalization compared to random sampling and semi-supervised baselines.
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