On September 1st 2023 at 14.00, Geletaw Sahle will defend his PhD entitled “DEVELOPING CLINICAL DECISION SUPPORT INSTRUMENTS FOR THE POINT-OF-CARE IN LOW RESOURCE SETTINGS”.

Everybody is invited to attend the presentation at the Room E.0.05.

Clinical decision support systems (CDSSs) have been shown to assist clinical decision making in healthcare while also enabling timely and appropriate integrated care services. The clinical pathway (CP), in particular, delivers and outlines an optimal logical path and plan of care from assessment to treatment at the primary and secondary health care level. Clinical pathways are increasingly used in routine patient care to maintain care process standardization, improve patient outcomes, reduce costs, and empower local healthcare practitioners. However, these clinical decision support and/or clinical pathway systems have remained out of reach for low-resource settings (LRSs). In LRSs, following the paper-based clinical guideline is a traditional practice and the only choice utmost. The service is suboptimal and challenged to deliver accurate and adequate evidence for decision making. Furthermore, low clinical competence, limited diagnostic capabilities, high turnover and low motivation are some of the challenges that most public health facilities in developing countries are facing on a day-to-day basis.

This dissertation demonstrates and develops computer-aided point-of-care decision support instruments for identifying referral and locally treatable cases. To develop CDSSs for LRSs, the overall need for the development of clinical decision support systems was initially assessed. Then, a state-of-the-art review was conducted to investigate design approaches for executable CPs at the point-of-care, and the results show that exploring a trade-off mechanism between knowledge-based and data-driven techniques is critical for promoting data-driven decision-making approaches. Next, an algorithm for the automated and dynamic generation of CPs was developed. The key principle of our proposed algorithm is that it operates with minimal clinical input and may be updated as new information becomes available, and it dynamically maps and validates the initial knowledge-based CP based on the local context and historical evidence in order to provide a multi-criteria decision analysis. The proposed solution was then deployed on an edge device, the Raspberry Pi 4 Model B, to provide a point-of-care clinical reference, data processing, and workflow generator, as well as an interactive data visualization and clinical guidance wizard for LRS. Finally, user acceptance of the CDSS at the point-of-care in LRSs was evaluated using 22 parameters organized into six major categories, namely ease of use, system quality, information quality, decision changes, process changes, and user acceptance. A follow-up interview, on the other hand, indicated a variety of reasons for disagreement based on the neutral, disagree, and strongly disagree responses. Furthermore, the overall acceptability was simulated using partial least squares structural equation modeling, and a variety of factors impacting the acceptance of the CDSS in LRSs were examined. In all, the key features of the CDSSs are able to provide low-cost, automated, adaptable, interactive, and applicable CPs for LRSs. A wider scale evaluation and longitudinal measurements, including CDSS usage frequency, speed of operation and impact on intervention time have not been included in the thesis work, because they require a larger deployment in daily practice.

Growth of personnel: 5 staff members: Jacques, Jean (assistants) Ingrid Sansens and André Pletinckx (technicians), as secretary Gilberte Lievens and Oscar Steenhaut (HoD).

On October 25th 2024 at 16:00, Yuqing Yang will defend their PhD entitled “CRAFTING EFFECTIVE VISUAL EXPLANATIONS BY ATTRIBUTING THE IMPACT OF DATASETS, ARCHITECTURES AND DATA COMPRESSION TECHNIQUES”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

Explainable Artificial Intelligence (XAI) plays an important role in modern AI research, motivated by the desire for transparency and interpretability within AI-driven decision-making. As AI systems become more advanced and complicated, it becomes increasingly important to ensure they are reliable, responsible, and ethical. These imperatives are particularly acute in domains where stakes are high, such as medical diagnostics, autonomous driving, and security frameworks.

In computer vision, XAI aims to provide understandable, straightforward explanations for AI model predictions, allowing users to grasp the decision-making processes of these complex systems. Visualizations such as saliency maps are frequently employed to identify input data regions significantly impacting model predictions, thus enhancing user understanding of AI visual data analysis. However, there are still concerns about the effectiveness of visual explanations, especially regarding their robustness, trustworthiness, and human-friendliness.

Our research aims to advance this field by evaluating how various factors—such as the diversity of datasets, the architecture of models, and techniques for data compression—influence the effectiveness of visual explanations in AI applications. Through thorough analysis and careful refinement, we strive to enhance these explanations, ensuring they are both highly informative and accessible to users in diverse XAI applications.

During our evaluation process, we conduct a detailed investigation using both automatic metrics and subjective evaluation methods to assess the effectiveness of visual explanations thoroughly. Automatic metrics, such as task performance and localization accuracy, provide quantifiable measures of the effectiveness of these explanations in real-world scenarios. For subjective evaluation, we have developed a framework named SNIPPET, which enables a detailed and user-oriented assessment of visual explanations. Additionally, our research explores how these objective metrics correlate with subjective human judgments, aiming to integrate quantitative data with the more nuanced, qualitative feedback from users. Ultimately, our goal is to provide comprehensive insights into the practical aspects of XAI methodologies, particularly focusing on their implementation in the field of computer vision.

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.

On March 18th 2024 at 17::00, Pieter Boonen will defend their PhD entitled “4D-CT for Detailed Diagnosis of Peripheral Arterial Disease and Diabetic Foot Ulcers. Is it worth our time?”.

Everybody is invited to attend the presentation at the Auditorium Piet Brouwer (Faculty of Medicine and Pharmacy), or digitally via this link.

 Peripheral arterial disease affects over 230 million people globally, causing arterial narrowing in the legs which can lead to a reduced blood flow and tissue damage. When combined with poorly managed diabetes, it can lead to serious issues like diabetic foot ulcers that may require amputation if untreated.
The current imaging methods, such as computed tomography angiography and magnetic resonance angiography lack hemodynamic information on the blood flow and tissue perfusion which can be crucial for diagnosing vascular disease and treatment monitoring.
This PhD thesis explores the application of a new technique called 4D-CT for diagnosing peripheral arterial disease and diabetic foot ulcers. By capturing multiple CT volumes over the same structure, the injected contrast bolus can be tracked through arteries and tissue.
Through a series of phantom and clinical studies, this thesis explored the feasibility of applying 4D-CT to measure and visualise the arterial blood flow and to examine the effect of peripheral arterial disease on the blood flow. An automated image processing pipeline was developed to segment arteries and tissues, facilitating the quantification of blood flow. The results of this thesis highlight the potential of 4D-CT in the diagnosis of vascular disease.

The Weight of the Cloud: Navigating Digital Mediation, Human Meaning, and Planetary Responsibility
Friday 10 April 2026
Vrije Universiteit Brussel – Auditorium I.2.02

The Centre for Ethics and Humanism (EtHu) warmly invites faculty members, researchers, and Master and Research Master students to the 2026 EtHu Research Day: The Weight of the Cloud: Navigating Digital Mediation, Human Meaning, and Planetary Responsibility.

This research day brings together philosophical, ethical, ecological, and technological perspectives to reflect on the implications of contemporary cloud-based technologies. While digital life is often imagined as light and immaterial, the infrastructures that sustain it—from data centres to global resource extraction—carry significant existential, social, and ecological weight. The event offers a space for critical discussion on how digital mediation reshapes human meaning and responsibility in a computational world.

The programme features keynote lectures by Prof. Dr. Vincent Blok (Erasmus University Rotterdam) and Prof. Dr. ir. Johan Stiens (Vrije Universiteit Brussel), as well as paper presentations by Aaron A. Bernstein (Georgia College & State University), Deborah Marber (De Montfort University), Massimiliano Simons and Joe Litobarski (Maastricht University), Amanda Platek (University of Copenhagen), Daniel Bjorklund (Western University), and Agostino Cera (Università di Ferrara).

The research day is open to faculty members, researchers, and Master and Research Master students. We warmly encourage you to share this invitation within your network.

Register here
More information is available via the EtHu website.

We hope to welcome many of you on 10 April.