4 papers
Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging
Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6
Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming a…
nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation
Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6
Semantic segmentation is crucial for various biomedical applications, yet its reliance on large annotated datasets presents a bottleneck due to the high cost and specialized expert…
nnInteractive: Redefining 3D Promptable Segmentation
Fabian Isensee, Maximilian Rokuss, Lars Krämer +10
Accurate and efficient 3D segmentation is essential for both clinical and research applications. While foundation models like SAM have revolutionized interactive segmentation, thei…
Code and Pixels: Multi-Modal Contrastive Pre-training for Enhanced Tabular Data Analysis
Kankana Roy, Lars Krämer, Sebastian Domaschke +4
Learning from tabular data is of paramount importance, as it complements the conventional analysis of image and video data by providing a rich source of structured information that…