arXiv:2606.15611v1 Announce Type: cross Abstract: 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 ac
The proliferation of visual foundation models and the increasing need for efficient medical image analysis are converging to make semi-supervised learning more viable for complex tasks like PET/CT segmentation.
This development can significantly reduce the annotation burden and cost associated with medical image analysis, accelerating the deployment of AI in critical healthcare applications like oncology.
The proposed mutual distillation framework offers a more robust and adaptable method for leveraging foundation models in semi-supervised medical image segmentation, potentially making advanced diagnostics more accessible.
- · Medical AI developers
- · Oncology departments
- · Patients needing cancer diagnosis/treatment planning
- · Medical imaging companies
- · Manual image annotation services
- · Traditional supervised learning approaches for medical imaging
Improved efficiency and accuracy in PET/CT image segmentation for cancer diagnosis and treatment planning.
Reduced healthcare costs associated with specialized medical image analysis and faster turnaround times for diagnostic results.
Broader adoption of AI in clinical settings could lead to more personalized and effective cancer therapies via advanced quantitative analysis.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI