Ziping Jiang
Social Behavioral Phenotyping of Drosophila with a 2D-3D Hybrid CNN Framework
Jiang, Ziping; Chazot, Paul L.; Celebi, M. Emre; Crookes, Danny; Jiang, Richard
Authors
Abstract
Behavioural phenotyping of drosphila is an important means in biological and medical research to identify genetic, pathologic or psychologic impact on animal behviour. Automated behavioural phenotyping from videos has been a desired capability that can waive long-time boring manual work in behavioral analysis. In this paper, we introduced deep learning into this challenging topic, and proposed a new 2D+3D hybrid CNN framework for drosphila’s social behavioural phenotyping. In the proposed multitask learning framework, action detection and localization of drosphila jointly is carried out with action classification, and a given video is divided into clips with fixed length. Each clip is fed into the system and a 2-D CNN is applied to extract features at frame level. Features extracted from adjacent frames are then connected and fed into a 3-D CNN with a spatial region proposal layer for classification. In such a 2D+3D hybrid framework, drosophila detection at the frame level enables the action analysis at different durations instead of a fixed period. We tested our framework with different base layers and classification architectures and validated the proposed 3D CNN based social behavioral phenotyping framework under various models, detectors and classifiers.
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 8, 2019 |
Online Publication Date | May 15, 2019 |
Publication Date | 2019 |
Deposit Date | May 7, 2019 |
Publicly Available Date | May 28, 2019 |
Journal | IEEE Access |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 7 |
Pages | 67972-67982 |
DOI | https://doi.org/10.1109/access.2019.2917000 |
Public URL | https://durham-repository.worktribe.com/output/1302413 |
Files
Published Journal Article
(5.5 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Published Journal Article (Advance online version)
(932 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
Advance online version This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
You might also like
Histamine receptors in GtoPdb v.2023.1
(2023)
Journal Article
Machine Learning based Biological Ageing Estimation: A Survey
(2022)
Book Chapter
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search