{"slug": "must-pet-multimodal-self-supervised-learning-across-tracers-for-whole-body-pet", "title": "MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation", "summary": "Researchers introduced MUST-PET, a multimodal self-supervised learning framework for whole-body PET-CT lesion segmentation, which uses context-aware masked reconstruction across FDG and PSMA tracers. In tests on multi-institutional pan-cancer datasets, MUST-PET reduced reconstruction error, improved segmentation over training from scratch, and performed well with limited labels and on unseen external datasets, showing potential for label-efficient, generalizable cancer imaging.", "body_md": "arXiv:2608.19666v1 Announce Type: new\nAbstract: Deep learning-based whole-body PET-CT lesion segmentation can support cancer staging, treatment planning, and response assessment, but generalization is limited by scarce annotations and domain shifts. Self-supervised learning (SSL) can address these challenges but remains underexplored in pan-cancer, multi-tracer PET-CT. In this work, we propose MUST-PET (MUltimodal Self-Supervised learning across Tracers), a multimodal, multi-tracer SSL framework for generalizable whole-body PET-CT lesion segmentation. MUST-PET is trained and validated on a diverse, multi-institutional collection of pan-cancer PET-CT scans acquired with FDG and prostate-specific membrane antigen (PSMA)-targeted radiotracers. MUST-PET uses context-aware masked reconstruction, where one modality is partially masked and reconstructed using complementary information from both PET and CT. The pretrained model is subsequently fine-tuned with labeled samples and evaluated for reconstruction quality, lesion segmentation, label efficiency, and generalizability across independent held-out datasets. MUST-PET reduces reconstruction error, improves lesion segmentation over training from scratch, and performs well with limited labeled data and on unseen external datasets, demonstrating the potential of multi-tracer SSL for label-efficient, generalizable whole-body PET-CT. segmentation.", "url": "https://wpnews.pro/news/must-pet-multimodal-self-supervised-learning-across-tracers-for-whole-body-pet", "canonical_source": "https://arxiv.org/abs/2608.19666", "published_at": "2026-08-21 04:00:00+00:00", "updated_at": "2026-08-21 04:17:04.091044+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["MUST-PET"], "alternates": {"html": "https://wpnews.pro/news/must-pet-multimodal-self-supervised-learning-across-tracers-for-whole-body-pet", "markdown": "https://wpnews.pro/news/must-pet-multimodal-self-supervised-learning-across-tracers-for-whole-body-pet.md", "text": "https://wpnews.pro/news/must-pet-multimodal-self-supervised-learning-across-tracers-for-whole-body-pet.txt", "jsonld": "https://wpnews.pro/news/must-pet-multimodal-self-supervised-learning-across-tracers-for-whole-body-pet.jsonld"}}