65 citations · 173 across the 27 of their papers we have counts for
6 papers · 1 filter
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Gregor Koehler, Tassilo Wald, Constantin Ulrich +6
Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot…
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
Silvia D. Almeida, Carsten T. Lüth, Tobias Norajitra +7
Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic Obstructive Pulmonary Disease (COPD) is a pri…
Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency
Tassilo Wald, Constantin Ulrich, Fabian Isensee +4
Independently trained machine learning models tend to learn similar features. Given an ensemble of independently trained models, this results in correlated predictions and common f…
Taming Detection Transformers for Medical Object Detection
Marc K. Ickler, Michael Baumgartner, Saikat Roy +2
The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures. To support c…
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Saikat Roy, Tassilo Wald, Gregor Koehler +5
Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting. With the recent introduction of the Segment Anything…
MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
Constantin Ulrich, Fabian Isensee, Tassilo Wald +3
The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmen…