A diagnostic platform powered by generative adversarial networks,
using active reinforcement learning
to speed up training
and minimize the knowledge transfer
effort of human doctors.
We are currently active
in cardio, respiratory
A semi-supervised learning AI
able to learn
from small labeled datasets augmenting their dimension,
and predicting possible variations or noise that could be met in the future, so to make diagnosis robust
in real world settings.
HOW IT WORKS
The Active Reinforcement Learning platform detects the most informative samples of a clinical dataset and submits them to Medical Doctors for labeling.
Then, the GANs platform propagates labels from small, manually labelled image datasets to all the unlabelled ones, so that the diagnostic AI can be trained to identify conditions without
the need of costly and time consuming human labelling (semi-supervised learning).
STAY IN TOUCH
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 876145.