100 citations · 226 across the 21 of their papers we have counts for
21 papers
Do Medical Foundation Models Generalize on the African Brain?
Kaouther Mouheb, Gonzalo Esteban Mosquera Rojas, Juancito van Leeuwen +2
Medical foundation models (FMs) are increasingly used for brain MRI analysis. However, their evaluation remains dominated by high-resource datasets, leaving generalization to Afric…
Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
Kaouther Mouheb, Amos Pomp, Antoine Manenti +9
Objectives: Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large language models (LLMs) on Dut…
TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
Marawan Elbatel, Mohamed Ghonim, Jiaji Mao +62
Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings acro…
Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages
Douwe J. Spaanderman, Karthik Prathaban, Petr Zelina +20
Large language models (LLMs) are increasingly used to extract structured information from free-text clinical records, but prior work often focuses on single tasks, limited models,…
Evaluating the Fairness of Neural Collapse in Medical Image Classification
Kaouther Mouheb, Marawan Elbatel, Stefan Klein +1
Deep learning has achieved impressive performance across various medical imaging tasks. However, its inherent bias against specific groups hinders its clinical applicability in equ…
Prior-knowledge-informed deep learning for lacune detection and quantification using multi-site brain MRI
Bo Li, Jeroen de Bresser, Wiro Niessen +5
Lacunes of presumed vascular origin, also referred to as lacunar infarcts, are important to assess cerebral small vessel disease and cognitive diseases such as dementia. However, v…