Project GRAPHINIUS – Interactive Graph Research Framework

Project MAKEpatho Machine Learning & Knowledge Extraction in Digital Pathology

Based on the ICT-2011.9.5 - FET Flagship "IT Future of Medicine" and in a joint effort together with BBMRI.at and the ADOPT project, we are working on making novel information accessible to a human expert in digital pathology.

Project TUGROVIS – Tumor-Growth Machine Learning

#KANDINSKYPatterns our Swiss-Knife for the study of explainable-AI

KANDINSKYPatterns our Swiss Knife for studying explainbale AI are mathematically describable, simple self-contained hence controllable test data sets for the development, validation and training of explainability in artificial intelligence.

FWF Project Reference Model of Explainable AI for the Medical Domain

The FWF project P-32554 "A reference model of explainable Artificial Intelligence for the Medical Domain" will provide important contributions to the international machine learning community, i.e. develop a library of explanatory patterns and a novel grammar how these can be combined, and will define criteria/benchmarks for explainability and principles to measure effectiveness of explainability and explainability guidelines a mapping of human understanding with machine explanations and deploying an open explanatory framework along with a set of benchmarks and open data to stimulate and inspire further research in transparent ML.

EU Project FeatureCloud (Federated Machine Learning)

The project’s ground-breaking novel cloud-AI infrastructure only exchanges learned representations (the feature parameters theta θ, hence the name “feature cloud”) which are anonymous by default (no hassle with “real medical data” – no ethical issues) - the data remain in safe harbours where they are and belong.

Project iML interactive Machine Learning with the Human-in-the-Loop

In this project we follow the HCI-KDD approach, i.e. with the human expert in the machine learning loop and opening the black box to a glass box!

EU Project HEAP – Human Exposome Assessment Platform

The project’s ground-breaking novel cloud-AI infrastructure only exchanges learned representations (the feature parameters theta θ, hence the name “feature cloud”) which are anonymous by default (no hassle with “real medical data” – no ethical issues) - the data remain in safe harbours where they are and belong.

AUGMENTOR

The Augmentor is a data augmentation library for machine learning, deep learning, in Python and Julia