1.4k citations · 3.1k across the 15 of their papers we have counts for
15 papers
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…
Optimization of Approximate Maps for Linear Systems Arising in Discretized PDEs
Rishad Islam, Arielle Carr, Colin Jacobs
Generally, discretization of partial differential equations (PDEs) creates a sequence of linear systems with well-known and structured sparsity…
Nodule detection and generation on chest X-rays: NODE21 Challenge
Ecem Sogancioglu, Bram van Ginneken, Finn Behrendt +16
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep lea…
Transfer learning from a sparsely annotated dataset of 3D medical images
Gabriel Efrain Humpire-Mamani, Colin Jacobs, Mathias Prokop +2
Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance…
Kidney abnormality segmentation in thorax-abdomen CT scans
Gabriel Efrain Humpire Mamani, Nikolas Lessmann, Ernst Th. Scholten +3
In this study, we introduce a deep learning approach for segmenting kidney parenchyma and kidney abnormalities to support clinicians in identifying and quantifying renal abnormalit…
Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks
Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier +2
Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes…