使用主动采样的组织病理学数据高效对比学习
Data Efficient Contrastive Learning in Histopathology using Active Sampling
AI总结：
提出一种利用小型代理网络主动采样信息丰富样本的方法，在组织病理学对比学习中减少93%样本需求和62%训练时间，同时保持与传统自监督学习相同的性能。
摘要：
基于深度学习（DL）的诊断系统可以在数字病理学中提供准确且稳健的定量分析。这些算法需要大量带标注的训练数据，但由于组织病理学图像的高分辨率，这在病理学中是不切实际的。因此，人们提出了自监督方法，利用临时设计的预文本任务来学习特征。自监督训练过程使用大型未标注数据集，这使得学习过程耗时。在这项工作中，我们提出了一种新方法，使用一个小型代理网络主动从训练集中采样信息丰富的成员，将样本需求减少93%，训练时间减少62%，同时保持与传统自监督学习方法相同的性能。代码可在 https://github.com/Reasat/data_efficient_cl 获取。
英文摘要：
Deep learning (DL) based diagnostics systems can provide accurate and robust quantitative analysis in digital pathology. These algorithms require large amounts of annotated training data which is impractical in pathology due to the high resolution of histopathological images. Hence, self-supervised methods have been proposed to learn features using ad-hoc pretext tasks. The self-supervised training process uses a large unlabeled dataset which makes the learning process time consuming. In this work, we propose a new method for actively sampling informative members from the training set using a small proxy network, decreasing sample requirement by 93% and training time by 62% while maintaining the same performance of the traditional self-supervised learning method. The code is available on https://github.com/Reasat/data_efficient_cl
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。
