A Pixel-Level Uncertainty-Aware Consistency Learning Framework for Enhancing Feature Compensation In Missing-Modality Glioma Segmentation
To develop a pixel-level uncertainty-aware consistency (UA-Cons) learning framework to optimize the feature compensation behavior of deep neural networks in scenarios where multi-parametric MRI modalities are incomplete.
Proffered Program · Diagnostic and Interventional Radiology Physics