Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data

1 code implementation • 31 Jan 2024 • Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva, Amol Verma, Fahad Razak, Nicolas Papernot, Bo wang

In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability.

Mortality Prediction Privacy Preserving

A Model for Multi-View Residual Covariances based on Perspective Deformation

no code implementations • 1 Feb 2022 • Alejandro Fontan, Laura Oliva, Javier Civera, Rudolph Triebel

In this work, we derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups.