12 citations · 15 across the 14 of their papers we have counts for
7 papers · 1 filter
TRUE-Colon: Exposing a Consistent Transfer Asymmetry in Real-Time Polyp Detection
Sebastian Doerrich, Andreas Franz Schwab, Francesco Di Salvo +3
Computer-aided detection (CADe) systems for colonoscopy promise to reduce clinical miss rates, yet reliable real-world deployment remains elusive. This translational gap stems in p…
MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification
Sebastian Doerrich, Daniel Würtinger, Francesco Di Salvo +2
Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end…
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…
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…
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…