Seminar Details
2026-07-02 (13:00) : 2 INGI seminars in one (J. Vanderhaeghen & A. Fierens)
At Shannon (Maxwell - a.105)
Duration: 60 minutes
Organized by Computer Science and Engineering
Speakers :
Amaury Fierens (ICTEAM)
,
and
Juliette Vanderhaeghen (ICTEAM)
Abstract :
By Juliette Vanderhaeghen:
Towards Personalized Tumor Growth Modeling with Physics-Informed Neural Networks
Abstract:
Cancer remains a major global health challenge, highlighting the urgent need for more personalized treatment strategies. Mathematical models, in particular PDE-based approaches, provide a powerful framework to describe tumor dynamics. However, classical numerical methods can be computationally expensive and require careful calibration, while purely data-driven models often lack interpretability and demand large amounts of data.
Physics-Informed Neural Networks (PINNs) offer a promising alternative by embedding physical laws directly into the training process, enabling both efficient simulations and the inference of patient-specific parameters from limited data.
In this presentation, I present our current work on the use of PINNs to model tumor growth dynamics. First, I will demonstrate how PINNs can reconstruct tumor evolution and estimate key parameters from partial synthetic observations. Then I will present their application to multicellular tumor spheroids as a controlled experimental system. These spheroids generate spatially and temporally resolved data under varying treatment conditions, making them a valuable testbed for model validation.
By Amaury Fierens:
Self-Alignment and Reranking for Large-Scale Medical Coding in Open-Source Clinical NLP
Abstract:
Medical coding is a challenging large-scale linking problem: clinical mentions must be mapped to highly ambiguous terminologies containing millions of possible codes, such as UMLS. While large language models are increasingly used in clinical NLP, they are not yet a fully satisfactory solution for this task, especially when reliability, traceability, terminology coverage, and precise code selection are required.
The presentation is structured in three parts. First, I will introduce the complexity of medical coding and why it remains difficult for both automated systems and clinicians. Second, I qill discuss an effective technical approach based on self-alignment pretraining with contrastive learning, as in SapBERT, combined with candidate reranking through cross-encoders, rule-based signals, and hierarchical information. Finally, drawing on the open-source work carried out this year by two master’s students, I will discuss how effective NLP components can be integrated into practical tools that support medical annotation and coding workflows.
Towards Personalized Tumor Growth Modeling with Physics-Informed Neural Networks
Abstract:
Cancer remains a major global health challenge, highlighting the urgent need for more personalized treatment strategies. Mathematical models, in particular PDE-based approaches, provide a powerful framework to describe tumor dynamics. However, classical numerical methods can be computationally expensive and require careful calibration, while purely data-driven models often lack interpretability and demand large amounts of data.
Physics-Informed Neural Networks (PINNs) offer a promising alternative by embedding physical laws directly into the training process, enabling both efficient simulations and the inference of patient-specific parameters from limited data.
In this presentation, I present our current work on the use of PINNs to model tumor growth dynamics. First, I will demonstrate how PINNs can reconstruct tumor evolution and estimate key parameters from partial synthetic observations. Then I will present their application to multicellular tumor spheroids as a controlled experimental system. These spheroids generate spatially and temporally resolved data under varying treatment conditions, making them a valuable testbed for model validation.
By Amaury Fierens:
Self-Alignment and Reranking for Large-Scale Medical Coding in Open-Source Clinical NLP
Abstract:
Medical coding is a challenging large-scale linking problem: clinical mentions must be mapped to highly ambiguous terminologies containing millions of possible codes, such as UMLS. While large language models are increasingly used in clinical NLP, they are not yet a fully satisfactory solution for this task, especially when reliability, traceability, terminology coverage, and precise code selection are required.
The presentation is structured in three parts. First, I will introduce the complexity of medical coding and why it remains difficult for both automated systems and clinicians. Second, I qill discuss an effective technical approach based on self-alignment pretraining with contrastive learning, as in SapBERT, combined with candidate reranking through cross-encoders, rule-based signals, and hierarchical information. Finally, drawing on the open-source work carried out this year by two master’s students, I will discuss how effective NLP components can be integrated into practical tools that support medical annotation and coding workflows.