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Found 5 papers, 3 papers with code
Counterfactual Generative Modeling with Variational Causal Inference
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, facial images) and covariates are relatively limited.
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.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Adversarial Policy Gradient for Deep Learning Image Augmentation
The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and variance of the training dataset.
Distance Map Loss Penalty Term for Semantic Segmentation
We propose to use distance maps, derived from ground truth masks, to create a penalty term, guiding the network's focus towards hard-to-segment boundary regions.
