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Explainable AI

Explainable AI (XAI) aims to render model behavior understandable by humans, which can be seen as an intermediate step in extracting causal relations from correlative patterns. Due to the high risk of possible fatal decisions in image-based clinical diagnostics, it is necessary to integrate explainable AI into these safety-critical systems.

Explainable AI in Helmholtz Imaging Modalities:

Expert

Lukas Klein

Deutsches Krebsforschungszentrum - Heidelberg

Skills:
Explanatory Methods, Interpretable Representation-learning, Model uncertainty, Probabilistic AI, Bayesian NN
Projects:
Improving Explainability through Disentangled Feature Representation, XAI in Multiple Instance Learning, Explaining Model-Uncertainty

In Helmholtz Imaging Modalities
➤Modalities: 0 ➤Applications: 2 ➤Instruments: 0 ➤Labs: 1 ➤Facilities: 0 ➤Centers: 1 ➤Solutions: 1

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Center

Deutsches Krebsforschungszentrum (DKFZ)

More than 450,000 people are diagnosed with cancer each year in Germany. Cancer is a disease that poses enormous challenges to research, because every cancer is different and its course can vary immensely even from …

In Helmholtz Imaging Modalities
➤Modalities: 18 ➤Applications: 6 ➤Instruments: 6 ➤Experts: 20 ➤Labs: 4 ➤Facilities: 0 ➤Solutions: 9

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Solution

Multi-path Explanations Library (m-pax_lib)

This repository contains a framework which allows for enhanced model interpretability and model robustness. The framework utilizes interpretable disentangled representations for downstream-task prediction which are in a second step visualized and evaluated through a multipath-attribution …

In Helmholtz Imaging Modalities
➤Modalities: 0 ➤Applications: 1 ➤Instruments: 0 ➤Experts: 1 ➤Labs: 1 ➤Facilities: 0 ➤Centers: 1