41 citations · 137 across the 34 of their papers we have counts for
6 papers · 1 filter
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Xiangde Luo, Jia Fu, Yunxin Zhong +39
Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is c…
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…
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…
Transformer Utilization in Medical Image Segmentation Networks
Saikat Roy, Gregor Koehler, Michael Baumgartner +4
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convo…
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…
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
Saikat Roy, Gregor Koehler, Constantin Ulrich +5
There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achi…