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20222025
most citedDiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling

4 citations · 4 across the 5 of their papers we have counts for

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2 papers · 1 filter

eess.IV2023

Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN

Jonas Utz, Tobias Weise, Maja Schlereth +5

Annotating nuclei in microscopy images for the training of neural networks is a laborious task that requires expert knowledge and suffers from inter- and intra-rater variability, e…

eess.IV2022

Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising

Fabian Wagner, Mareike Thies, Laura Pfaff +10

Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usual…