Tingying Peng

Tingying Peng’s Helmholtz AI young investigator group’s goal to create new AI methods to help life scientists and pathologists analyse microscopic images more quantitatively and efficiently, allowing them to extract more knowledge. We would like to develop deep learning methods to address several unique challenges for biomedical imaging, including:

Domain differences between medical images and natural images, where most of the deep learning techniques originate;

Scarcity of high-quality annotated data for efficient network training; and

The need for explainable algorithms, which usually conflicts with the ‘black-box’ nature of deep neural networks.

The key research strategy we proposed is interpretable deep learning, which blends domain knowledge such as conventional model-based methods and deep learning-based algorithms in a ‘maximise a posterior’ (MAP) manner. This combined approach will leverage the advantages of both model types, allowing us to make more accurate predictions while also shedding light on the underlying mechanisms of neural networks for making those predictions.