I-Corps: Development of a machine vision system for high-throughput computational behavioral analysis

这个i-Corps项目的更广泛的影响/商业潜力是，通过开发用于自动化视频分析和行为监控的机器视觉算法，有望彻底改变生物医学研究。生命科学的许多领域都需要对大量视频数据进行人工标注。然而，复杂行为的稳健量化带来了一个主要的瓶颈，由于与手动注释行为相关的固有偏见和挑战，行为研究中出现了许多争议。其中许多问题将通过使用客观的量化电脑化技术而得到解决。该项目的目标是利用机器学习和计算机视觉来分析大量数据，并发现肉眼看不见的新的行为视觉特征。这个i-Corps项目建议大规模开发、测试和研究应用算法和软件，以实现行为监控和分析的自动化。我们已经开发了一个初步的高通量系统，用于自动监测和分析啮齿动物的行为。这种方法利用了深度学习领域的最新发展，深度学习是机器学习的一个分支，它使由多个处理阶段组成的神经网络能够学习具有多个抽象级别的视觉表征。当从视频中对一只单独居住的老鼠的典型行为进行评分时，当前的系统可以准确地识别无数正常和异常的啮齿动物行为，其程度与人类无法区分。拟议的活动将通过解决生物、认知和心理学研究中视觉识别的基本问题，使算法更接近商业部署。

The broader impact/commercial potential of this I-Corps project is the promise to revolutionize bio-medical research via the development of machine vision algorithms for automating video analysis and behavioral monitoring. Many areas of the life sciences demand the manual annotation of large amounts of video data. However, the robust quantification of complex behaviors imposes a major bottleneck and a number of controversies in behavioral studies have arisen because of the inherent biases and challenges associated with the manual annotation of behavior. Many of these issues will be resolved with the use of objective quantitative computerized techniques. The goal of the project is to leverage machine learning and computer vision to analyze large volumes of data and discover novel visual features of behavior that are literally hidden to the naked eye.This I-Corps project proposes the large-scale development, testing, and research application of algorithms and software for automating the monitoring and analysis of behavior. We have developed an initial high-throughput system for the automated monitoring and analysis of rodent behavior. The approach capitalizes on recent developments in the area of deep learning, which is a branch of machine learning that enables neural networks composed of multiple processing stages to learn visual representations with multiple levels of abstraction. The current system accurately recognizes a myriad of normal and abnormal rodent behaviors at a level indistinguishable from human when scoring typical behaviors of a singly housed mouse from video. The proposed activities will bring algorithms closer to commercial deployment by addressing the fundamental problem of visual recognition in biological, cognitive, and psychological research.