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测的机器视觉算法来彻底改变生物医学研究的承诺。生命科学的许多领域需要对大量视频数据进行手动注释。然而，强大的量化复杂的行为施加了一个主要的瓶颈，并在行为研究中出现了一些争议，因为与行为的手动注释相关的固有偏见和挑战。其中许多问题将通过使用客观的定量计算机化技术来解决。 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，并发现肉眼无法看到的行为的新视觉特征。该I-Corps项目提出了大规模开发，测试和研究应用算法和软件，以自动监控和分析行为。我们已经开发了一个初始的高通量系统，用于啮齿动物行为的自动化监测和分析。该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层次的视觉表示。目前的系统准确地识别了无数的正常和异常的啮齿动物行为的水平与人类无法区分时，评分的典型行为的一个单独圈养的小鼠从视频。拟议的活动将通过解决生物学、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.