22 citations · 42 across the 12 of their papers we have counts for
5 papers · 1 filter
Benchmarking Foundation Models for Renal Lesion Stratification in CT
Hartmut Häntze, Sarah de Boer, Myrthe Buser +7
The rapid proliferation of open-source medical foundation models (FMs) raises a practical question: how well do their pre-trained representations transfer to clinically relevant bu…
Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language
Michelle Stegeman, Lena Philipp, Fennie van der Graaf +20
Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data modalities, a single model can be…
ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation
Rianne Weber, Niels Rocholl, Max de Grauw +3
In this study, we present ULS+, an enhanced version of the Universal Lesion Segmentation (ULS) model. The original ULS model segments lesions across the whole body in CT scans give…
Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework
Sarah de Boer, Hartmut Häntze, Kiran Vaidhya Venkadesh +9
Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an importa…
Divide to Conquer: A Field Decomposition Approach for Multi-Organ Whole-Body CT Image Registration
Xuan Loc Pham, Mathias Prokop, Bram van Ginneken +1
Image registration is an essential technique for the analysis of Computed Tomography (CT) images in clinical practice. However, existing methodologies are predominantly tailored to…