BINDER - Improving Bio-inspired Deep Learning for Radiomics


The project's objective is to improve the state of the art in Radiomics analysis of breast and rectal cancer, using existing and novel Machine Learning (ML) and Deep Learning (DL) methods. Radiomics is an emerging field of oncologic imaging. It aims at extracting large amounts of informative features from standard of care medical images, and analysing them for improving predictive power in precision medicine. Currently, many phases of the Radiomics workflow are not automated, and thus time-consuming, subjective, and prone to errors. The project will contribute at the development of faster, more objective, and accurate Radiomics models, that can be used to tailor treatment options and thus reduce toxicity and improve clinical outcomes. To achieve this goal, among others, Convolutional NNs will be used and compared to novel DL methods, that are expected to outperform the state of the art, thanks to their competitiveness in terms of evolvability and their ability of limiting overfitting.


Funded by



Universidade Nova de Lisboa, Universidade de Lisboa, Universidade de Coimbra, Fundação Champalimaud

Total budget

239 696,00 €

Local budget

13 518,00 €


Radiomics, Machine Learning, Deep Learning, Genetic Programming

Start Date


End Date