20 citations · 36 across the 6 of their papers we have counts for
6 papers
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…
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…
Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
Georg Wölflein, Dyke Ferber, Asier R. Meneghetti +6
Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting…
Deep Multiple Instance Learning with Distance-Aware Self-Attention
Georg Wölflein, Lucie Charlotte Magister, Pietro Liò +2
Traditional supervised learning tasks require a label for every instance in the training set, but in many real-world applications, labels are only available for collections (bags)…
HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks
Georg Wölflein, In Hwa Um, David J Harrison +1
The presence and density of specific types of immune cells are important to understand a patient's immune response to cancer. However, immunofluorescence staining required to ident…
Determining Chess Game State From an Image
Georg Wölflein, Ognjen Arandjelović
Identifying the configuration of chess pieces from an image of a chessboard is a problem in computer vision that has not yet been solved accurately. However, it is important for he…