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Found 18 papers, 4 papers with code
Two Stream Active Query Suggestion for Active Learning in Connectomics
As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics.
SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization
Recent algorithms for image manipulation detection almost exclusively use deep network models.
Class Incremental Online Streaming Learning
We propose a novel approach (CIOSL) for the class-incremental learning in an \emph{online streaming setting} to address these challenges.
Multilayer Dense Connections for Hierarchical Concept Classification
Classification is a pivotal function for many computer vision tasks such as object classification, detection, scene segmentation.
VideoSSL: Semi-Supervised Learning for Video Classification
To minimize the dependence on a large annotated dataset, our proposed semi-supervised method trains from a small number of labeled examples and exploits two regulatory signals from unlabeled data.
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding
For video and volumetric data understanding, 3D convolution layers are widely used in deep learning, however, at the cost of increasing computation and training time.
Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets
The few methods that computes direction along with contact location have only been demonstrated to work on either dyadic (most common in vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on both types.
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals.
Morphological Error Detection in 3D Segmentations
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology.
Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures
We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures.
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation
The prospect of neural reconstruction from Electron Microscopy (EM) images has been elucidated by the automatic segmentation algorithms.
What Properties are Desirable from an Electron Microscopy Segmentation Algorithm
The prospect of neural reconstruction from Electron Microscopy (EM) images has been elucidated by the automatic segmentation algorithms.
Enforcing Label and Intensity Consistency for IR Target Detection
Each pixel is associated with a label which indicates whether it is a target or background pixel.
Annotating Synapses in Large EM Datasets
Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics.
Small Sample Learning of Superpixel Classifiers for EM Segmentation- Extended Version
Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms.
A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation
In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron.
Submodularity of a Set Label Disagreement Function
A set label disagreement function is defined over the number of variables that deviates from the dominant label.
Machine learning of hierarchical clustering to segment 2D and 3D images
We aim to improve segmentation through the use of machine learning tools during region agglomeration.
