M2OCNN: Many-to-One Collaboration Neural Networks for simultaneously multi-modal medical image synthesis and fusion.
Journal:
Computer methods and programs in biomedicine
PMID:
39908634
Abstract
BACKGROUND AND OBJECTIVE: Acquiring comprehensive information from multi-modal medical images remains a challenge in clinical diagnostics and treatment, due to complex inter-modal dependencies and missing modalities. While cross-modal medical image synthesis (CMIS) and multi-modal medical image fusion (MMIF) address certain issues, existing methods typically treat these as separate tasks, lacking a unified framework that can generate both synthesized and fused images in the presence of missing modalities.