Edward De Brouwer
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View article: Leveraging hand-crafted radiomics on multicenter FLAIR MRI for predicting disability worsening in people with multiple sclerosis
Leveraging hand-crafted radiomics on multicenter FLAIR MRI for predicting disability worsening in people with multiple sclerosis Open
Background Multiple sclerosis (MS) is an autoimmune disease of the central nervous system, leading to varying degrees of functional impairment. Conventional tools, such as the Expanded Disability Status Scale (EDSS), lack sensitivity to su…
View article: Neural FIM: Bridging Statistical Manifolds and Generative Modeling through Fisher Geometry
Neural FIM: Bridging Statistical Manifolds and Generative Modeling through Fisher Geometry Open
View article: Manifold filter-combine networks
Manifold filter-combine networks Open
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). Our filter-combine framework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and natura…
View article: Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data
Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data Open
View article: Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death
Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death Open
Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing organ shortage. While recent technological advances in organ transplantation have increa…
View article: RAG-Enhanced Collaborative LLM Agents for Drug Discovery
RAG-Enhanced Collaborative LLM Agents for Drug Discovery Open
Recent advances in large language models (LLMs) have shown great potential to accelerate drug discovery. However, the specialized nature of biochemical data often necessitates costly domain-specific fine-tuning, posing major challenges. Fi…
View article: Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Progression in People with Multiple Sclerosis
Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Progression in People with Multiple Sclerosis Open
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that results in varying degrees of functional impairment. Conventional tools, such as the Expanded Disability Status Scale (EDSS), lack sensitivity to su…
View article: Learning Predictive Checklists with Probabilistic Logic Programming
Learning Predictive Checklists with Probabilistic Logic Programming Open
Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpretability and ease of use have led to their adoption for …
View article: Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states
Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states Open
Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell …
View article: Deep Learning Unlocks the True Potential of Organ Donation after Circulatory Death with Accurate Prediction of Time-to-Death
Deep Learning Unlocks the True Potential of Organ Donation after Circulatory Death with Accurate Prediction of Time-to-Death Open
Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing organ shortage. While recent technological advances in organ transplantation have increa…
View article: Convergence of Manifold Filter-Combine Networks
Convergence of Manifold Filter-Combine Networks Open
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). The filter-combine framework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and natura…
View article: Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma
Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma Open
View article: Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study
Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study Open
Background Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be ado…
View article: Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study
Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study Open
Background The integrity and reliability of clinical research outcomes rely heavily on access to vast amounts of data. However, the fragmented distribution of these data across multiple institutions, along with ethical and regulatory barri…
View article: The magnitude vector of images
The magnitude vector of images Open
The magnitude of a finite metric space has recently emerged as a novel invariant quantity, allowing to measure the effective size of a metric space. Despite encouraging first results demonstrating the descriptive abilities of the magnitude…
View article: Atom-Level Optical Chemical Structure Recognition with Limited Supervision
Atom-Level Optical Chemical Structure Recognition with Limited Supervision Open
View article: Atom-Level Optical Chemical Structure Recognition with Limited Supervision
Atom-Level Optical Chemical Structure Recognition with Limited Supervision Open
Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet, existing methods for chemical structure recognition do …
View article: Benchmarking Observational Studies with Experimental Data under Right-Censoring
Benchmarking Observational Studies with Experimental Data under Right-Censoring Open
Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT). A majo…
View article: Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study (Preprint)
Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study (Preprint) Open
BACKGROUND The integrity and reliability of clinical research outcomes rely heavily on access to vast amounts of data. However, the fragmented distribution of these data across multiple institutions, along with ethical and regulatory barr…
View article: The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research
The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research Open
Background Investigating low-prevalence diseases such as multiple sclerosis is challenging because of the rather small number of individuals affected by this disease and the scattering of real-world data across numerous data sources. These…
View article: BLIS-Net: Classifying and Analyzing Signals on Graphs
BLIS-Net: Classifying and Analyzing Signals on Graphs Open
Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much less work has been done on signal classification, where the data consists of many functions (referre…
View article: Manifold Filter-Combine Networks
Manifold Filter-Combine Networks Open
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). Our filter-combine framework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and natura…
View article: Inferring dynamic regulatory interaction graphs from time series data with perturbations
Inferring dynamic regulatory interaction graphs from time series data with perturbations Open
Complex systems are characterized by intricate interactions between entities that evolve dynamically over time. Accurate inference of these dynamic relationships is crucial for understanding and predicting system behavior. In this paper, w…
View article: A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction.
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction. Open
Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology a…
View article: A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction Open
Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology a…
View article: The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research (Preprint)
The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research (Preprint) Open
BACKGROUND Investigating low-prevalence diseases such as multiple sclerosis is challenging because of the rather small number of individuals affected by this disease and the scattering of real-world data across numerous data sources. Thes…
View article: Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection
Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection Open
Training object detection models usually requires instance-level annotations, such as the positions and labels of all objects present in each image. Such supervision is unfortunately not always available and, more often, only image-level i…
View article: Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections Open
Neural ordinary differential equations (Neural ODEs) are an effective framework for learning dynamical systems from irregularly sampled time series data. These models provide a continuous-time latent representation of the underlying dynami…
View article: Learning predictive checklists from continuous medical data
Learning predictive checklists from continuous medical data Open
Checklists, while being only recently introduced in the medical domain, have become highly popular in daily clinical practice due to their combined effectiveness and great interpretability. Checklists are usually designed by expert clinici…
View article: Learning dynamical systems from data: A simple cross-validation perspective, Part III: Irregularly-sampled time series
Learning dynamical systems from data: A simple cross-validation perspective, Part III: Irregularly-sampled time series Open