Search Results for author:
Found 7 papers, 3 papers with code
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
Deep vessel segmentation based on a new combination of vesselness filters
Vascular segmentation represents a crucial clinical task, yet its automation remains challenging.
A Novel Autoencoders-LSTM Model for Stroke Outcome Prediction using Multimodal MRI Data
Patient outcome prediction is critical in management of ischemic stroke.
CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients
For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS).
Modeling and hexahedral meshing of cerebral arterial networks from centerlines
We proposed a vessel model based on penalized splines to overcome the limitations inherent to the centerline representation, such as noise and sparsity.
Privacy Assessment of Federated Learning using Private Personalized Layers
While this scheme has been proposed as local adaptation to improve the accuracy of the model through local personalization, it has also the advantage to minimize the information about the model exchanged with the server.
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks
However, nothing prevents the service provider to infer private and sensitive information about a user such as health or demographic attributes. In this paper, we present DySan, a privacy-preserving framework to sanitize motion sensor data against unwanted sensitive inferences (i. e., improving privacy) while limiting the loss of accuracy on the physical activity monitoring (i. e., maintaining data utility).
