使用深度和广度多尺度递归网络识别突触
Identifying Synapses Using Deep and Wide Multiscale Recursive Networks
摘要：
在这项工作中，我们提出了一个学习框架，用于识别突触使用深度和广度的多尺度递归（DAWMR）网络，以前在图像分割应用中考虑。我们将这种方法应用于无脊椎动物苍蝇脑组织的电子显微镜数据。通过直接从数据中学习特征，我们能够实现对依赖于一小部分手工设计特征的现有技术的显著改进。我们表明，该系统可以减少所需的手动注释量，无论是收购的训练数据，以及推断检测的验证。
英文摘要：
In this work, we propose a learning framework for identifying synapses using a deep and wide multi-scale recursive (DAWMR) network, previously considered in image segmentation applications. We apply this approach on electron microscopy data from invertebrate fly brain tissue. By learning features directly from the data, we are able to achieve considerable improvements over existing techniques that rely on a small set of hand-designed features. We show that this system can reduce the amount of manual annotation required, in both acquisition of training data as well as verification of inferred detections.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
