瑞典皇家理工学院Emma Lundberg等研究人员报道了人类蛋白质图谱的分类分析的竞赛结果。2019年11月28日出版的《自然—方法学》发表了这一结果。

研究人员表示，从显微镜图像中精确定位亚细胞蛋白的定位对训练有素的人员来说很容易，但是自动化却很困难。

基于人类蛋白质图谱的图片集，研究人员举办了一场竞赛，以确定深度学习解决方案来解决此任务。挑战包括在高度失衡的类型上进行培训，以及预测每个图像多个标签。

在3个多月的时间里，共有2172个团队参加了比赛。尽管在流行的网络和培训技术上趋于一致，但解决方案之间仍存在很大差异。

参与者应用了策略来修改神经网络和损失函数，增强数据并使用预训练的网络。获胜的模型远胜于人们以前在蛋白质定位模式的多标签分类上所做的工作，效率提高约20％。这些模型可以用作分类器用于注释新图像、特征提取器以测量模式相似度或用于广泛生物学应用的预训练网络。

Title: Analysis of the Human Protein Atlas Image Classification competition

Author: Wei Ouyang, Casper F. Winsnes, Martin Hjelmare, Anthony J. Cesnik, Lovisa kesson, Hao Xu, Devin P. Sullivan, Shubin Dai, Jun Lan, Park Jinmo, Shaikat M. Galib, Christof Henkel, Kevin Hwang, Dmytro Poplavskiy, Bojan Tunguz, Russel D. Wolfinger, Yinzheng Gu, Chuanpeng Li, Jinbin Xie, Dmitry Buslov, Sergei Fironov, Alexander Kiselev, Dmytro Panchenko, Xuan Cao, Runmin Wei, Yuanhao Wu, Xun Zhu, Kuan-Lun Tseng, Zhifeng Gao, Cheng Ju, Xiaohan Yi, Hongdong Zheng, Constantin Kappel, Emma Lundberg

Issue&Volume: 2019-11-28

Abstract: Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.

DOI: 10.1038/s41592-019-0658-6

Source: https://www.nature.com/articles/s41592-019-0658-6