iLearn
Interpretable Learning Methods for Immunotherapy
Started
April 1, 2018
Status
Completed
Share this project
Abstract
People
Collaborators
SDSC Team:
No items found.
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
Presentation
Download Presentation
Gallery
Annexe
Additionnal resources
Bibliography
Publications
Related Pages
More projects
CLIMIS4AVAL
In Progress
Real-time cleansing of snow and weather data for operational avalanche forecasting
Energy, Climate & Environment
News
Latest news
March 20, 2024
Efficient and scalable graph generation through iterative local expansion
Efficient and scalable graph generation through iterative local expansion
Have you ever considered the complexity of generating large-scale, intricate graphs akin to those that represent the vast relational structures of our world? Our research introduces a pioneering approach to graph generation that tackles the scalability and complexity of creating such expansive, real-world graphs.
March 6, 2024
RAvaFcast | Automating regional avalanche danger prediction in Switzerland
RAvaFcast | Automating regional avalanche danger prediction in Switzerland
RAvaFcast is a data-driven model pipeline developed for automated regional avalanche danger forecasting in Switzerland. It combines a recently proposed classifier for avalanche danger prediction at weather stations with a spatial interpolation model and a novel aggregation strategy to estimate the danger levels in predefined wider warning regions, ultimately assembled as an avalanche bulletin.
February 6, 2024
PassGPT | Using language models to enhance password security
PassGPT | Using language models to enhance password security
PassGPT is a Large Language Model for password generation trained on leaked passwords, which can outperform existing methods based on generative adversarial networks by guessing twice as many unseen passwords.
Contact us
Let’s talk Data Science
Do you need our services or expertise?
Contact us for your next Data Science project!