iLearn
Interpretable Learning Methods for Immunotherapy
Started
April 1, 2018
Status
Completed
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Abstract
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PI | Partners:
Co-PIs:
- Pascal Frossard (EPFL)
- Olivier Michielin (CHUV)
description
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
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