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Found 13 papers, 3 papers with code
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology.
Forecasting Whole-Brain Neuronal Activity from Volumetric Video
Large-scale neuronal activity recordings with fluorescent calcium indicators are increasingly common, yielding high-resolution 2D or 3D videos.
How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease.
High-Precision Automated Reconstruction of Neurons with Flood-filling Networks
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites.
Electron Microscopy Image Segmentation
Image Reconstruction
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Adversarial Image Alignment and Interpolation
Finally, we show how these two capabilities can be combined in order to produce artificial isotropic volumes from anisotropic image volumes using a super-resolution adversarial alignment and interpolation approach.
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images.
Morphological Error Detection in 3D Segmentations
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology.
Flood-Filling Networks
State-of-the-art image segmentation algorithms generally consist of at least two successive and distinct computations: a boundary detection process that uses local image information to classify image locations as boundaries between objects, followed by a pixel grouping step such as watershed or connected components that clusters pixels into segments.
Combinatorial Energy Learning for Image Segmentation
We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image.
Learned versus Hand-Designed Feature Representations for 3d Agglomeration
For image recognition and labeling tasks, recent results suggest that machine learning methods that rely on manually specified feature representations may be outperformed by methods that automatically derive feature representations based on the data.
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling
Feedforward multilayer networks trained by supervised learning have recently demonstrated state of the art performance on image labeling problems such as boundary prediction and scene parsing.
Learning to Agglomerate Superpixel Hierarchies
An agglomerative clustering algorithm merges the most similar pair of clusters at every iteration.
Natural Image Denoising with Convolutional Networks
We present an approach to low-level vision that combines two main ideas: the use of convolutional networks as an image processing architecture and an unsupervised learning procedure that synthesizes training samples from specific noise models.
