论文标题
针对小型COVID-19 CT扫描数据集的针对分类的自我监督
Targeted Self Supervision for Classification on a Small COVID-19 CT Scan Dataset
论文作者
论文摘要
传统上，卷积神经网络需要人类标记的大量数据进行训练。已经提出了自我监督作为处理少量标记数据的一种方法。这项研究的目的是确定自我监督是否可以提高小型COVID-19 CT扫描数据集上的分类性能。这项研究还旨在确定拟议的自我监督策略（针对性的自我监督）是否是COVID-19成像数据集的可行选择。总共进行了10种实验，将提出的自我监督方法的分类性能与不同量的数据进行了比较。通过提出的自我监督策略进行的实验比其非自我监督对应的实验要好得多。与没有自我监督相比，通过完整的自我监督，我们的准确性几乎增加了8％。结果表明，自我监督可以改善小型COVID-19 CT扫描数据集上的分类性能。可以在此链接上找到针对目标自我监督的代码
Traditionally, convolutional neural networks need large amounts of data labelled by humans to train. Self supervision has been proposed as a method of dealing with small amounts of labelled data. The aim of this study is to determine whether self supervision can increase classification performance on a small COVID-19 CT scan dataset. This study also aims to determine whether the proposed self supervision strategy, targeted self supervision, is a viable option for a COVID-19 imaging dataset. A total of 10 experiments are run comparing the classification performance of the proposed method of self supervision with different amounts of data. The experiments run with the proposed self supervision strategy perform significantly better than their non-self supervised counterparts. We get almost 8% increase in accuracy with full self supervision when compared to no self supervision. The results suggest that self supervision can improve classification performance on a small COVID-19 CT scan dataset. Code for targeted self supervision can be found at this link: https://github.com/Mewtwo/Targeted-Self-Supervision/tree/main/COVID-CT
