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Found 5 papers, 1 papers with code
Cost-efficient segmentation of electron microscopy images using active learning
Our experiments on three different electron microscopy datasets show that active learning can improve segmentation quality by 10 to 15% in terms of Jaccard score compared to standard randomized sampling.
Bayesian Deconvolution of Scanning Electron Microscopy Images Using Point-spread Function Estimation and Non-local Regularization
Microscopy is one of the most essential imaging techniques in life sciences.
Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy
Biological membranes are one of the most basic structures and regions of interest in cell biology.
Domain Adaptive Segmentation in Volume Electron Microscopy Imaging
In this work, we extend recently proposed classification DA techniques to an encoder-decoder layout and propose a novel method that adds a reconstruction decoder to the classical encoder-decoder segmentation in order to align source and target encoder features.
Lower bounds on the robustness to adversarial perturbations
The input-output mappings learned by state-of-the-art neural networks are significantly discontinuous.
