5 citations · 10 across the 7 of their papers we have counts for
7 papers
Three-dimensional end-to-end deep learning for brain MRI analysis
Radhika Juglan, Marta Ligero, Zunamys I. Carrero +9
Deep learning (DL) methods are increasingly outperforming classical approaches in brain imaging, yet their generalizability across diverse imaging cohorts remains inadequately asse…
Abnormality-Driven Representation Learning for Radiology Imaging
Marta Ligero, Tim Lenz, Georg Wölflein +3
To date, the most common approach for radiology deep learning pipelines is the use of end-to-end 3D networks based on models pre-trained on other tasks, followed by fine-tuning on…
Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning
Tim Lenz, Peter Neidlinger, Marta Ligero +3
Representation learning of pathology whole-slide images (WSIs) has primarily relied on weak supervision with Multiple Instance Learning (MIL). This approach leads to slide represen…
In-context learning enables multimodal large language models to classify cancer pathology images
Dyke Ferber, Georg Wölflein, Isabella C. Wiest +8
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this proce…
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology
Tim Lenz, Omar S. M. El Nahhas, Marta Ligero +1
Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations…
Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology
Omar S. M. El Nahhas, Georg Wölflein, Marta Ligero +5
Deep Learning (DL) can predict biomarkers directly from digitized cancer histology in a weakly-supervised setting. Recently, the prediction of continuous biomarkers through regress…
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology
Omar S. M. El Nahhas, Marko van Treeck, Georg Wölflein +9
Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computation…