13 citations · 22 across the 8 of their papers we have counts for
6 papers · 1 filter
Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models
Tim Lenz, Maurice Heide, Marco Gustav +2
Computational pathology foundation models (PFMs) have advanced whole-slide image analysis. However, their size and inference cost hinder local deployment in pathology departments.…
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…
A deep learning framework for efficient pathology image analysis
Peter Neidlinger, Tim Lenz, Sebastian Foersch +24
Artificial intelligence (AI) has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images (WSIs). However, current methods are computa…
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…
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…