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.
Sensory input from multiple sources is crucial for robust and coherent human perception.
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.)
In our endeavor to create a navigation assistant for the BVI, we found that existing Reinforcement Learning (RL) environments were unsuitable for the task.
The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million.
Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored.
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.
This study proposed an exhaustive stable/reproducible rule-mining algorithm combined to a classifier to generate both accurate and interpretable models.
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients.
Multivariate classification methods using explanatory and predictive models are necessary for characterizing subgroups of patients according to their risk profiles.