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Found 6 papers, 0 papers with code
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease
To this end, we propose an end-to-end deep learning framework based on self-supervised learning for the segmentation and quantification of dopaminergic neurons in PD animal models.
Multiclass Semantic Segmentation to Identify Anatomical Sub-Regions of Brain and Measure Neuronal Health in Parkinson's Disease
Automated segmentation of anatomical sub-regions with high precision has become a necessity to enable the quantification and characterization of cells/ tissues in histology images.
Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning
We also show that pretraining on extended (but not labeled) brain MRI data outperforms pretraining on natural images.
DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression for Real-World Clinical Applications
Our proposed model integrates high dimensional MRI features from a 3D convolutional neural network with other data modalities, including clinical and demographic information, to predict the future trajectory of patients.
Analysis by Synthesis: 3D Object Recognition by Object Reconstruction
We introduce an efficient "brute-force" approach to inference that searches through a large number of candidate reconstructions, returning the optimal one.
Analyzing 3D Objects in Cluttered Images
We use a morphable model to capture 3D within-class variation, and use a weak-perspective camera model to capture viewpoint.
