1. Explainable AI in Deep Brain Medicine (Afsaneh Asaei & Ozgur Polat)

This talk summarizes the key elements of explainable AI in development of medicine for brain pathology. The project is a joint collaboration between Digital Product School of UnternehmerTUM and the Schoen Clinic of Neurology at Munich for objective evaluation of patients with Parkinson’s disease. We will compare two paradigms of deep learning: One fully explainable and rich for defining the biomarkers of neurodegeneration contrasted with a typical architecture for end-to-end learning.

2. Computer Vision Methods for Skin Cancer Recognition (Karol Przystalski)

Pattern recognition of images is one of the most popular approaches that is used in machine learning solutions supporting medical doctors. We show to use image processing methods to do simple image analysis to find skin cancer cases. In the next step, we use neural networks and simple white-box methods to recognize skin cancer patterns on multilevel images. As it is a medical solution we show the process of commercialization of an AI solution for medtech and explain what kind of explainable AI methods can be used to be compliant with the FDA requirements.

3. How AI Can Help Us Unlock Nature’s Immense Therapeutic Potential (Sona Chandra)

Despite millenia of knowledge on the vast therapeutic potential of plants, we have explored less than 1% of the molecules found within this kingdom of life. Traditional methods for understanding the activity of natural products require laborious, resource-intensive experimental assays. We will discuss how machine learning can be used to accelerate the discovery of bioactive compounds, with a concrete example of how we illuminated the MOA of polyphenols with regards to their anti-inflammatory properties.