Interpretable Learning Methods for Immunotherapy – iLearn

Problem:

Study of immune-tumour interaction

• Need of expert pathologists to understand complex spatio-temporal patterns
• Identification of tumour cell evolution patterns, understanding of the disease pathogenesis
• Understanding of the spatial interaction between cells
• Design of new interpretable NN architectures
• Design of new spatio-temporal models
• Performance evaluation and model validation

Solution:

• Fitting a graphical model in each layer of the NN
• Design new NN that will explicitly include priors and constraints
• Dynamic NN: combination of CNN and RNN
• Application of machine learning algorithms to mice data and then to tumor response on humans

Impact:

• Development of new interpretable data-driven algorithms.
• Development of new interpretable data-driven algorithm to understand the tumor microenvironment