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

Gallery

Annexe

Additionnal resources

Bibliography

Publications

Related Pages

More projects

ML4FCC

In Progress
Machine Learning for the Future Circular Collider Design
Big Science Data

CLIMIS4AVAL

In Progress
Real-time cleansing of snow and weather data for operational avalanche forecasting
Energy, Climate & Environment

SEMIRAMIS

Completed
AI-augmented architectural design
Energy, Climate & Environment

4D-Brains

In Progress
Extracting activity from large 4D whole-brain image datasets
Biomedical Data Science

News

Latest news

Efficient and scalable graph generation through iterative local expansion
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.
RAvaFcast | Automating regional avalanche danger prediction in Switzerland
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.
PassGPT | Using language models to enhance password security
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!