“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)
On May 16th 2025 at 16:00, Pooria Iranian will defend their PhD entitled “FLUORESCENCE-LIFETIME ENDOSCOPY WITH A TIME-GATED CAMERA”.
Everybody is invited to attend the presentation in room I.0.01 or online via this link.
The diagnosis and treatment of cancer remain complex and challenging, particularly during surgical procedures where accurately distinguishing between malignant and healthy tissue is critical. Conventional imaging modalities such as MRI and CT often fail to provide the real-time, high-contrast visualization necessary for precise tumour removal.
Fluorescence imaging has emerged as a valuable technique, offering real-time, high-sensitivity visualization of tissues. However, traditional fluorescence-intensity imaging is limited by the fluorophore’s intensity and differentiating between fluorophores with overlapping emission spectra. In contrast, Fluorescence Lifetime Imaging (FLT) provides a more reliable alternative. FLT is less susceptible to variations in fluorophore concentration and offers a more accurate representation of the tissue environment.
To enhance the accuracy of tumour resection during image-guided surgeries, our research team is developing an innovative imaging system based on the Current-Assisted Photonic Sampler (CAPS). This advanced camera enables real-time FLT imaging in the near-infrared (NIR) spectrum, specifically between 700–900 nm, which is particularly effective for deep tissue imaging.
By integrating this FLT technology into an endoscopic system incorporating standard white light imaging, we aim to provide surgeons with enhanced visual information. This dual-imaging capability enables better differentiation between malignant and benign tissues, which traditional white light endoscopy imaging systems cannot adequately achieve. Current systems often require separate RGB and FLT imaging cameras, leading to alignment issues and bulky configurations.
To address these limitations, we have developed a comprehensive endoscopy system that combines a rigid Hopkins endoscope with a state-of-the-art time-gated camera utilizing the CAPS sensor (tauCAM). This system capitalizes on the strengths of FLT to improve intraoperative imaging.
Additionally, we have introduced a novel deep learning-based algorithm, FLTCNN, which accurately estimates fluorescence lifetimes without requiring the system’s intrinsic parameters. The algorithm significantly reduces computational demands using a few data points, making real-time FLT imaging feasible.
Moreover, we have introduced a technique known as time-sequential RGB imaging. In this method, the surgical scene is sequentially illuminated with red, green, blue, and NIR light pulses, enabling the simultaneous acquisition of both RGB and FLT images. These images can be overlaid to give surgeons comprehensive real-time visual feedback.
Our research addresses the critical need for efficient recording of IRFs and dramatically reduces the time points required for lifetime estimation. By combining time-sequential RGB imaging with FLT in a single-camera system, we overcome challenges related to image alignment and equipment bulkiness. This development promises an advancement in surgical imaging technology, offering improved precision and better outcomes for cancer patients.
A full immersive experience of Augmented Reality for neurosurgical planning and real-time intervention, demonstrated by Taylor on the FARI immersive CAVE during the Agoria HealthTech roundtable event June 17th , 2024.
𝐌𝐞𝐝𝐓𝐞𝐜𝐡 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫 𝟐𝟎𝟐𝟔 𝐢𝐬 𝐨𝐟𝐟𝐢𝐜𝐢𝐚𝐥𝐥𝐲 𝐎𝐍 🕺with TWO ETRO participants!
🤙 Detailed description: https://lnkd.in/egx7zM4X
𝐎𝐑𝐈𝐎𝐍 with Ioana-Teodora Ouatu & Johan Stiens
𝐓𝐀𝐑𝐂 with Taylor Frantz
⚡️hub.brussels supports hashtag#entrepreneurs who make an hashtag#impact
On 23 October 2026 at 16:00, Brent De Weerdt will defend their PhD entitled “Interpretable Transformer Networks for Compressed Sensing, Representation Learning, and Distributed Source Coding”.
Everybody is invited to attend the presentation in room LIC.0.04 or online via this link.
The current trend in artificial intelligence, and deep learning in particular, is to design increasingly large and complex models. Although these models achieve high accuracy, they require large datasets, high computational budgets, and often remain difficult to interpret. This thesis addresses these limitations by leveraging theoretical knowledge and data priors to design lightweight, performant, and inherently interpretable Transformer-based deep learning models.
The work first considers signal acquisition through compressed sensing. Using deep unfolding, the thesis designs neural networks for signal reconstruction from compressed measurements. By incorporating sparsity and temporal correlation, it develops interpretable Transformer models that reconstruct signals more accurately and efficiently than existing methods. The thesis then studies compact and privacy-preserving representations of human pose trajectories in video using contrastive learning and generative modelling, enabling anomaly detection through deviations from learned normal motion patterns. Finally, the thesis addresses distributed source coding, where correlated observations from multiple devices can be compressed more efficiently by exploiting inter-source dependencies. Building on LDPCA codes, it proposes a multi-rate Transformer decoder for Slepian-Wolf decoding that outperforms traditional decoders and reduces communication cost compared with alternative distributed compression methods.
Overall, the thesis develops deep learning architectures that incorporate signal processing, information theory, and data-specific prior knowledge, achieving state-of-the-art performance across several tasks while reducing the computational budget by at least an order of magnitude in most cases.
The Biomedical Engineering Master @ VUB accepts students with a bachelor or a Master in Engineering.
In case you do not have a full background in biomedical engineering, we consider you will need to perform a preparatory program consisting in several courses defined in a personalised study program for you.
Students who think they have had the same course before, have to fill out the form for exemption, (https://student.vub.be/en/ir#regulations-and-forms) and together with the transcripts send everything to the faculty secretary office.