BIOLab

Biomedical Intervention Optimisation lab

Modern machine learning algorithms have achieved unprecedented accuracy in image and video understanding tasks, via pure learning from data. These powerful abilities come at the price of enormous amounts of training data, memory, and computational requirements. Such resources are rarely available to real-time feedback systems in medical intervention and biomedical research.

Experts will join forces in the BIOLab across many fields including computer vision, reinforcement learning, neural architecture, deep learning and computational physics, and biomedical imaging. We will create high-efficiency, real-time, AI-driven feedback and control in biomedical applications. The focus is on improving the efficiency of machine learning algorithms by designing novel artificial neural network architectures, developing new reinforcement learning and generative algorithms, and incorporating biologically inspired neural network models. These newly developed concepts and algorithms will be applied to a wide range of problems in biomedical applications. Examples include optimizing tumour irradiation protocols with missing information, and limiting irradiation damage to delicate living samples in smart microscopy.  

The BIOLab is part of the TU Delft AI Labs programme.

BIOLab combines expertise from multiple imaging and machine learning domains.

Projects

Publications

Code & Datasets

The Team

Directors

Kristin Grußmayer

Faculty of AS
Lab director

Wendelin Böhmer

Faculty of EEMCS
Lab director

Zoltán Perkó

Faculty of AS
Lab director

Daan Brinks

Faculty of AS
Lab director

Nergis Tömen

Faculty of EEMCS
Lab director

PhD's

Laurens Engwegen

PhD student

Alejandro Castañeda

PhD student

Miyase Tekpinar

PhD student

Mikolaj Stryja

PhD student

Education

Courses

2024/2025 

2023/2024  

2022/2023  

2021/2022  

2020/2021  

2019/2020  

 

Master projects

Ongoing  

  • Smart Super-resolution Microscopy Data Acquisition Using Deep Learning, Nergis Tomen, Gijs Schout (2023/2024) 
  • A biophysical model for enhanced understanding of learning processes in the inferior olive, Daan Brinks, Jaume Abad i Villa (2023/2024) 
  • On- and off-target effects of non-normal perturbation dynamics in small and large recurrent cortical networks, Daan Brinks, Niek Rijnders (2023/2024) 
  • Machine-learning assisted Directed evolution for protein engineering, Daan Brinks, Rowan Brakel (2023/2024) 
  • Modelling of Physically accurate neural networks for applications in reinforcement learning, Daan Brinks, Ian van Vliet (2023/2024) 
  • Time steps in spiking neural networks, Nergis Tomen, Alex de Los Santos Subirats (2023/2024) 
  • Self-organized criticality in spiking networks of non-leaky integrator neurons, Nergis Tomen, Luca Frattini (2023/2024) 
  • NeuroBench: Benchmarking spiking and analog neural networks for primate tracking sata, Nergis Tomen, Paul Hueber (2022/2023) 
  • Making It Clear Using Vision Transformers in Multi-View Stereo on Specular and Transparent Materials, Nergis Tomen, Pieter Tolsma (2022/2023) 

Finished