A weakly supervised activity recognition framework for real-time synthetic biology laboratory assistance

Chandrashekhar Lavania, S. Thulasidasan, A. LaMarca, Jeffrey Scofield, J. Bilmes
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引用次数: 9

Abstract

We describe the design of a hybrid system -- a combination of a Dynamic Graphical Model (DGM) with a Deep Neural Network (DNN) -- to identify activities performed during synthetic biology experiments. The purpose is to provide real-time feedback to experimenters, thus helping to reduce human errors and improve experimental reproducibility. The data consists of unlabeled videos of recorded experiments and "weakly supervised" information (i.e., "theoretical" and asynchronous knowledge of sets of high level activity sequences in the experiment) used to train the system. Multiple activity sequences are modeled using a trellis, and deep features are extracted from video images. Model performance is accessed using real-time online statistical inference. The trellis incorporates variations during experiment execution, making our model very general and capable of high performance.
一个弱监督的实时合成生物学实验室辅助活动识别框架
我们描述了一个混合系统的设计-动态图形模型(DGM)与深度神经网络(DNN)的组合-以识别合成生物学实验期间执行的活动。目的是向实验人员提供实时反馈,从而帮助减少人为错误,提高实验的可重复性。数据包括录制实验的未标记视频和用于训练系统的“弱监督”信息(即实验中高水平活动序列集的“理论”和异步知识)。利用网格对多个活动序列进行建模,提取视频图像的深层特征。使用实时在线统计推断来访问模型性能。网格在实验执行过程中包含了变化,使我们的模型非常通用并且能够高性能。
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