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.
#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 interpretability guidelines in mapping 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 machine learning.
NOE FTI-22-I-004 Infrastructure for Testing AI-driven robot systems in complex environments
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!
AUGMENTOR
The Augmentor is a widely accepted data augmentation library for machine learning, deep learning, in Python and Julia
Project EMPAIA – Ecosystem for Pathology Diagnostics with AI Assistance
EMPAIA stands for Ecosystem for Pathology Diagnostics with AI Assistance
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.