12 citations · 15 across the 14 of their papers we have counts for
7 papers · 1 filter
Unsupervised Feature Orthogonalization for Learning Distortion-Invariant Representations
Sebastian Doerrich, Francesco Di Salvo, Christian Ledig
This study introduces unORANIC+, a novel method that integrates unsupervised feature orthogonalization with the ability of a Vision Transformer to capture both local and global rel…
An Embedding is Worth a Thousand Noisy Labels
Francesco Di Salvo, Sebastian Doerrich, Ines Rieger +1
The performance of deep neural networks scales with dataset size and label quality, rendering the efficient mitigation of low-quality data annotations crucial for building robust a…
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…
Rethinking model prototyping through the MedMNIST+ dataset collection
Sebastian Doerrich, Francesco Di Salvo, Julius Brockmann +1
The integration of deep learning based systems in clinical practice is often impeded by challenges rooted in limited and heterogeneous medical datasets. In addition, the field has…
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…