10 papers · 1 filter
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…
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…
EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging
Jan Tagscherer, Sarah de Boer, Lena Philipp +7
Developing foundation models in medical imaging requires continuous monitoring of downstream performance. Researchers are burdened with tracking numerous experiments, design choice…
Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language
Michelle Stegeman, Lena Philipp, Fennie van der Graaf +19
Medical foundation models show promise to learn broadly generalizable features from large, diverse datasets. This could be the base for reliable cross-modality generalization and r…
Kidney Cancer Detection Using 3D-Based Latent Diffusion Models
Jen Dusseljee, Sarah de Boer, Alessa Hering
In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabil…
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…