Search Results for author:
Found 11 papers, 8 papers with code
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings.
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Sensory input from multiple sources is crucial for robust and coherent human perception.
On self-supervised multi-modal representation learning: An application to Alzheimer's disease
In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.
Cross-Modal Information Maximization for Medical Imaging: CMIM
In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as the different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.)
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
In our endeavor to create a navigation assistant for the BVI, we found that existing Reinforcement Learning (RL) environments were unsuitable for the task.
A Survey of Mobile Computing for the Visually Impaired
The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million.
Learning to rank for censored survival data
Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored.
Distribution Matching Losses Can Hallucinate Features in Medical Image Translation
When the output of an algorithm is a transformed image there are uncertainties whether all known and unknown class labels have been preserved or changed.
Rule-Mining based classification: a benchmark study
This study proposed an exhaustive stable/reproducible rule-mining algorithm combined to a classifier to generate both accurate and interpretable models.
Deep Learning for Patient-Specific Kidney Graft Survival Analysis
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients.
L1 logistic regression as a feature selection step for training stable classification trees for the prediction of severity criteria in imported malaria
Multivariate classification methods using explanatory and predictive models are necessary for characterizing subgroups of patients according to their risk profiles.
