“Signal Processing in the AI era” was the tagline of this year’s IEEE International Conference on Acoustics, Speech and Signal Processing, taking place in Rhodes, Greece.
In this context, Brent de Weerdt, Xiangyu Yang, Boris Joukovsky, Alex Stergiou and Nikos Deligiannis presented ETRO’s research during poster sessions and oral presentations, with novel ways to process and understand graph, video, and audio data. Nikos Deligiannis chaired a session on Graph Deep Learning, attended the IEEE T-IP Editorial Board Meeting, and had the opportunity to meet with collaborators from the VUB-Duke-Ugent-UCL joint lab.
Featured articles:
- De Weerdt, B. et al., “Designing Transformer Networks for Sparse Recovery of Sequential Data Using Deep Unfolding” (https://ieeexplore.ieee.org/abstract/document/10094712)
- Yang X. et al., “Relevance Propagation through Deep Conditional Random Fields” (https://ieeexplore.ieee.org/abstract/document/10095075)
- Stergiou A. et al., “Play it back: Iterative attention for audio recognition” (https://ieeexplore.ieee.org/abstract/document/10096532, developed at the University of Bristol)
Lucas Moura Santana won the 2022-2023 IEEE SSCS Predoctoral Achievement Award
PROJECT: INTOWALL: detection of leaks and isolation in walls
Our buildings generate 35% of the CO2-emissions and 40% of the energy use. 75% of our buildings need to be energetically renovated. In order to do so, one needs to know what the status is of the current isolation, and if there are any water leaks. The Vrije Universiteit Brussel researches with a consortium of partners towards a unique, patented technique to look into the walls without breaking them. With a compact and mobile radar system you can “read” your walls. This project is part of the Smart Hub Cleantech, climate ambitions of the province and the communes.
Partners: Vrije Universiteit Brussel i.s.m. WTCB, Green Energy Park, BAM, ING, Flux50 en Alter Reim.
Nicolas Ospitia Patino has successfully defended his Ph.D. dissertation obtaining the greatest honors and congratulations from the jury! It’s been an incredible journey of dedication, hard work, and personal growth. He did his PhD in collaboration between MeMC and ETRO, supervised by Prof. Dimitris Aggelis (MeMC) and Prof. Johan Stiens, (ETRO), Congratulations to Nicolas for this result.
The introductory part will last approx. 45 minutes. It consists of a brief overview of weather and climate studies and will be followed by a (very) brief introduction of machine learning. The two subjects will be tied together by looking into the potential of AI techniques in the field.Lastly, two specific examples will be presented in more detail: (1) Attempts to replace the radiation scheme with a deep learning model to reduce computational cost and (2) Using U-nets and GAN's for super resolving (downscaling) over Germany.The talk will be held on Thursday 13/10/2022 at 09:30 am in the Marconi meeting room (2nd floor of Pleinlaan 9). It is also possible to follow the talk online using this link.We hope to see you there (or online)!
On April 2nd 2026 at 16:00, Sebastian Amador Sanchez will defend their PhD entitled “Advancing Landmark Localization through Deep Segmentation for Reliable Malalignment Assessment in Lower Limb Radiographs”.
Everybody is invited to attend the presentation in room I.2.01 or online via this link.
Knee osteoarthritis affects millions worldwide and is frequently associated with lower limb malalignment. Clinical assessment of malalignment relies on manual landmark identification in X-ray images, a time-consuming process prone to interobserver variability. While most automated approaches use regression-based deep learning, this thesis investigates image segmentation with circular masks centered on landmark locations as an alternative strategy.
First, we introduce a segmentation-guided coordinate regression framework that integrates a segmentation network with a coordinate regression branch, trained end-to-end. This hybrid approach improves localization accuracy over standard regression and increases robustness compared to standalone segmentation, thereby mitigating false positives and missed detections.
Second, we optimize segmentation-based methods by evaluating architectures, post-processing strategies, and mask sizes. A fully convolutional model trained with radius-15 masks, combined with adaptive threshold-based centroid extraction, outperformed conventional landmark localization approaches, with improved performance in knee phenotype classification.
Third, we propose a Siamese network trained with a contrastive loss for quality control that detects inaccurate predictions by comparing image patches to reference embeddings. The method reliably identifies errors exceeding 2.0 mm and estimates their magnitude, outperforming baseline methods.
Overall, this thesis advances the field of landmark localization and demonstrates its clinical relevance for the automated assessment of lower limb malalignment. Beyond landmark localization accuracy, our contributions address robustness and failure identification, two aspects that are often overlooked yet vital for future clinical deployment.
New video on the INTOWALL project .