2 citations · 3 across the 5 of their papers we have counts for
5 papers
Privacy-preserving datasets by capturing feature distributions with Conditional VAEs
Francesco Di Salvo, David Tafler, Sebastian Doerrich +1
Large and well-annotated datasets are essential for advancing deep learning applications, however often costly or impossible to obtain by a single entity. In many areas, including…
MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions
Francesco Di Salvo, Sebastian Doerrich, Christian Ledig
The integration of neural-network-based systems into clinical practice is limited by challenges related to domain generalization and robustness. The computer vision community estab…
Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization
Sebastian Doerrich, Francesco Di Salvo, Christian Ledig
Despite notable advancements, the integration of deep learning (DL) techniques into impactful clinical applications, particularly in the realm of digital histopathology, has been h…
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification
Sebastian Doerrich, Tobias Archut, Francesco Di Salvo +1
Traditional deep learning models implicity encode knowledge limiting their transparency and ability to adapt to data changes. Yet, this adaptability is vital for addressing user da…
unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features
Sebastian Doerrich, Francesco Di Salvo, Christian Ledig
We introduce unORANIC, an unsupervised approach that uses an adapted loss function to drive the orthogonalization of anatomy and image-characteristic features. The method is versat…