5 papers
Anatomically Consistent Cross-Contrast Super-Resolution of Anisotropic Brain T2w MRI
Mengqi Shen, Haicheng Wang, Meghna Trivedi +4
T2-weighted (T2w) brain MRI provides fluid-sensitive soft-tissue contrast that is important for neuro-oncology and radiotherapy planning. However, T2w scans are acquired with aniso…
Foundation Model-guided Iteratively Prompting and Pseudo-Labeling for Partially Labeled Medical Image Segmentation
Qiaochu Zhao, Wei Wei, David Horowitz +2
Automated medical image segmentation has achieved remarkable progress with fully labeled data. However, site-specific clinical priorities and the high cost of manual annotation oft…
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy
Jingyun Chen, David Horowitz, Yading Yuan
Background: Deep learning has potential to improve the efficiency and consistency of radiation therapy planning, but clinical adoption is hindered by the limited model generalizabi…
Decentralized Personalization for Federated Medical Image Segmentation via Gossip Contrastive Mutual Learning
Jingyun Chen, Yading Yuan
Federated Learning (FL) presents a promising avenue for collaborative model training among medical centers, facilitating knowledge exchange without compromising data privacy. Howev…
FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy
Jingyun Chen, Martin King, Yading Yuan
Dose prediction plays a key role in knowledge-based planning (KBP) by automatically generating patient-specific dose distribution. Recent advances in deep learning-based dose predi…