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Found 4 papers, 0 papers with code
Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging
Specifically, our adaptive fusion module aggregates 7T-like features derived from the LF image by a pre-trained network and then refines them to be effectively assimilable UHF guidance into LF image features.
A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis
Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately.
A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals
Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions.
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis
For building a source model generally applicable to various tasks, we pre-train the model in a self-supervised learning manner for masked encoding vector prediction as a proxy task, using a large-scale normal, healthy brain magnetic resonance imaging (MRI) dataset.
