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Found 6 papers, 3 papers with code
Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation
Alzheimer's disease is the most common cause of dementia that affects millions of lives worldwide.
Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations
However, the adversarial training with gradient-based attacks lacks diversity and does not generalize well to natural images and various attacks.
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases
Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients.
Patient Subtyping via Time-Aware LSTM Networks
We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes.
Ranked #4 on
Multivariate Time Series Forecasting
on USHCN-Daily
Asynchronous Multi-Task Learning
The models for each hospital may be different because of the inherent differences in the distributions of the patient populations.
Similarity Learning via Adaptive Regression and Its Application to Image Retrieval
The similarity between pairs of images can be measured by the distances between their high dimensional representations, and the problem of learning the appropriate similarity is often addressed by distance metric learning.
