Head and Tail Localization of C. elegans

01/12/2020
by   Mansi Ranjit Mane, et al.
6

C. elegans is commonly used in neuroscience for behaviour analysis because of it's compact nervous system with well-described connectivity. Localizing the animal and distinguishing between its head and tail are important tasks to track the worm during behavioural assays and to perform quantitative analyses. We demonstrate a neural network based approach to localize both the head and the tail of the worm in an image. To make empirical results in the paper reproducible and promote open source machine learning based solutions for C. elegans behavioural analysis, we also make our code publicly available.

READ FULL TEXT

page 2

page 3

page 4

research
06/12/2023

Feature Fusion from Head to Tail: an Extreme Augmenting Strategy for Long-Tailed Visual Recognition

The imbalanced distribution of long-tailed data poses a challenge for de...
research
04/10/2023

Head-tail Loss: A simple function for Oriented Object Detection and Anchor-free models

This paper presents a new loss function for the prediction of oriented b...
research
09/20/2023

Long-tail Augmented Graph Contrastive Learning for Recommendation

Graph Convolutional Networks (GCNs) has demonstrated promising results f...
research
07/16/2022

Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation

The current studies of Scene Graph Generation (SGG) focus on solving the...
research
06/23/2022

Learning To Generate Scene Graph from Head to Tail

Scene Graph Generation (SGG) represents objects and their interactions w...
research
08/19/2022

Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling

In an era of information explosion, recommendation systems play an impor...
research
02/03/2022

Machine Learning Solar Wind Driving Magnetospheric Convection in Tail Lobes

To quantitatively study the driving mechanisms of magnetospheric convect...

Please sign up or login with your details

Forgot password? Click here to reset