5 citations · 5 across the 2 of their papers we have counts for
3 papers · 1 filter
Prompt-Tuning SAM: From Generalist to Specialist with only 2048 Parameters and 16 Training Images
Tristan Piater, Björn Barz, Alexander Freytag
The Segment Anything Model (SAM) is widely used for segmenting a diverse range of objects in natural images from simple user prompts like points or bounding boxes. However, SAM's p…
N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture
Eva Höck, Tim-Oliver Buchholz, Anselm Brachmann +2
In recent years, neural network based image denoising approaches have revolutionized the analysis of biomedical microscopy data. Self-supervised methods, such as Noise2Void (N2V),…
Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation
Simon Reiß, Constantin Seibold, Alexander Freytag +2
Pixel-wise segmentation is one of the most data and annotation hungry tasks in our field. Providing representative and accurate annotations is often mission-critical especially for…