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A Multi-Pass Approach to Large-Scale Connectomics
Yaron Meirovitch, Alexander Matveev, Hayk Saribekyan +8
The field of connectomics faces unprecedented "big data" challenges. To reconstruct neuronal connectivity, automated pixel-level segmentation is required for petabytes of streaming…
RhoanaNet Pipeline: Dense Automatic Neural Annotation
Seymour Knowles-Barley, Verena Kaynig, Thouis Ray Jones +7
Reconstructing a synaptic wiring diagram, or connectome, from electron microscopy (EM) images of brain tissue currently requires many hours of manual annotation or proofreading (Ka…
Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures
Felix Gonda, Verena Kaynig, Ray Thouis +4
We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures. An interactive training scheme reduces the extremely…
LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister +1
Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden repre…
Context-guided diffusion for label propagation on graphs
Kwang In Kim, James Tompkin, Hanspeter Pfister +1
Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isotropic diffusion, which is induced by the commonly-used graph Laplacian regulariz…
Semi-supervised Learning with Explicit Relationship Regularization
Kwang In Kim, James Tompkin, Hanspeter Pfister +1
In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points. Ex…