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Found 4 papers, 1 papers with code
Unified Multi-Modal Image Synthesis for Missing Modality Imputation
Besides, we propose a Dynamic Feature Unification Module to integrate information from a varying number of available modalities, which enables the network to be robust to random missing modalities.
Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism
Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations.
Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window
Since the pandemic of COVID-19, several deep learning methods were proposed to analyze the chest Computed Tomography (CT) for diagnosis.
Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention
With an Uncertainty-Aware Module, this network can provide a Multi-Confidence Mask (MCM), pointing out regions with different segmentation uncertainty levels.
