1 citations · 1 across the 2 of their papers we have counts for
4 papers
Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining
José Teixeira, Pascal Klöckner, Diana Montezuma +5
In addition to evaluating tumor morphology using H&E staining, immunohistochemistry is used to assess the presence of specific proteins within the tissue. However, this is a costly…
Foundation Models in Medical Imaging: A Review and Outlook
Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu +10
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FM…
Training state-of-the-art pathology foundation models with orders of magnitude less data
Mikhail Karasikov, Joost van Doorn, Nicolas Känzig +5
The field of computational pathology has recently seen rapid advances driven by the development of modern vision foundation models (FMs), typically trained on vast collections of p…
Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction
Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur +3
To improve the prediction of cancer survival using whole-slide images and transcriptomics data, it is crucial to capture both modality-shared and modality-specific information. How…