402 citations · 923 across the 41 of their papers we have counts for
4 papers · 1 filter
Morphological Error Detection in 3D Segmentations
David Rolnick, Yaron Meirovitch, Toufiq Parag +5
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans,…
Deep Learning is Robust to Massive Label Noise
David Rolnick, Andreas Veit, Serge Belongie +1
Deep neural networks trained on large supervised datasets have led to impressive results in image classification and other tasks. However, well-annotated datasets can be time-consu…
Generative Compression
Shibani Santurkar, David Budden, Nir Shavit
Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we…
Toward Streaming Synapse Detection with Compositional ConvNets
Shibani Santurkar, David Budden, Alexander Matveev +4
Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images. Recent studies have success…