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Found 5 papers, 2 papers with code
Semi-Supervised Diffusion Model for Brain Age Prediction
Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data.
Rician likelihood loss for quantitative MRI using self-supervised deep learning
Results: Networks trained with NLR loss show higher estimation accuracy than MSE for the ADC and IVIM diffusion coefficients as SNR decreases, with minimal loss of precision or total error.
Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder
Therefore, this work stands as a contribution to the pertinent development of accurate and interpretable deep learning within medical imaging.
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
Stacking Gaussian Processes severely diminishes the model's ability to detect outliers, which when combined with non-zero mean functions, further extrapolates low non-parametric variance to low training data density regions.
NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification
When further applied to the task of predicting which patients with mild cognitive impairment will be diagnosed with Alzheimer's disease within two years, the model achieves state-of-the-art accuracy with no additional training.
