用AI自动识别老鼠胡须触碰，准确率达人类水平
WhACC: Whisker Automatic Contact Classifier with Expert Human-Level Performance
- 用ResNet50V2提取特征，LightGBM分类触碰事件
- 在百万帧上与真人标注一致率达99.5%
- 支持小样本微调，大幅减少人工标注时间
啮齿动物胡须系统对神经科学研究至关重要，尤其在皮层可塑性、学习、决策、感觉编码和感觉运动整合研究中。尽管已有自动化工具如Janelia Whisker Tracker，标注触碰事件仍需超过3小时/百万帧。为此，我们提出WhACC——一个用于头固定行为鼠高速视频中触碰期识别的Python工具包，采用ResNet50V2进行特征提取，结合LightGBM进行分类。性能在超过一百万帧上经三位专家标注验证，与人类标注者之间的触碰分类一致性达99.5%，与人与人之间的一致性相当。此外，我们提供定制化重训练接口，在16个单单元电生理记录数据集共四百万帧上验证有效。加入重训练后，百万帧数据集的人工标注时间从约333小时降至约6小时。
原文摘要 · Abstract (English)
The rodent vibrissal system is pivotal in advancing neuroscience research, particularly for studies of cortical plasticity, learning, decision-making, sensory encoding, and sensorimotor integration. Despite the advantages, curating touch events is labor intensive and often requires >3 hours per million video frames, even after leveraging automated tools like the Janelia Whisker Tracker. We address this limitation by introducing Whisker Automatic Contact Classifier (WhACC), a python package designed to identify touch periods from high-speed videos of head-fixed behaving rodents with human-level performance. WhACC leverages ResNet50V2 for feature extraction, combined with LightGBM for Classification. Performance is assessed against three expert human curators on over one million frames. Pairwise touch classification agreement on 99.5% of video frames, equal to between-human agreement. Finally, we offer a custom retraining interface to allow model customization on a small subset of data, which was validated on four million frames across 16 single-unit electrophysiology recordings. Including this retraining step, we reduce human hours required to curate a 100 million frame dataset from ~333 hours to ~6 hours.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。