Social Behavioral Phenotyping of Drosophila with a 2D-3D Hybrid CNN Framework

Abstract

Behavioural phenotyping of drosophila is an important means in biological and medical research to identify the genetic, pathologic, or psychological impact on animal behavior. Automated behavioral 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 drosophila's social behavioral phenotyping. In the proposed multi-task learning framework, action detection and localization of drosophila jointly are 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 the 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.