Background and objectives

Deep learning techniques are the state-of-the-art approach to solve image classification problems in biomedicine; however, they require the acquisition and annotation of a considerable volume of images. In addition, using deep learning libraries and tuning the hyperparameters of the networks trained with them might be challenging for several users. These drawbacks prevent the adoption of these techniques outside the machine-learning community. In this work, we present an Automated Machine Learning (AutoML) method to deal with these problems.

Methods

Our AutoML method combines transfer learning with a new semi-supervised learning procedure to train models when few annotated images are available. In order to facilitate the dissemination of our method, we have implemented it as an open-source tool called ATLASS. Finally, we have evaluated our method with two benchmarks of biomedical image classification datasets.

Results

Our method has been thoroughly tested both with small datasets and partially annotated biomedical datasets; and, it outperforms, both in terms of speed and accuracy, the existing AutoML tools when working with small datasets; and, might improve the accuracy of models up to a 10% when working with partially annotated datasets.

Conclusions

The work presented in this paper allows the use of deep learning techniques to solve an image classification problem with few resources. Namely, it is possible to train deep models with small, and partially annotated datasets of images. In addition, we have proven that our AutoML method outperforms other AutoML tools both in terms of accuracy and speed when working with small datasets.

中文翻译：

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借助转移和半监督学习，生物医学图像分类变得更加容易

背景和目标

深度学习技术是解决生物医学中图像分类问题的最先进方法。但是，它们需要获取和注释大量的图像。此外，使用深度学习库和调整经过它们训练的网络的超参数对于一些用户而言可能是挑战。这些缺点使机器学习社区无法采用这些技术。在这项工作中，我们提出了一种自动机器学习（AutoML）方法来解决这些问题。

方法

我们的AutoML方法将转移学习与新的半监督学习程序相结合，以在几乎没有带注释的图像的情况下训练模型。为了促进我们方法的传播，我们已将其实现为称为ATLASS的开源工具。最后，我们用生物医学图像分类数据集的两个基准评估了我们的方法。

结果

我们的方法已经通过小型数据集和部分注释的生物医学数据集进行了全面测试；在处理小型数据集时，在速度和准确性方面都优于现有的AutoML工具。并且使用部分注释的数据集时，可以将模型的准确性提高多达10％。

结论

本文提出的工作允许使用深度学习技术来解决资源少的图像分类问题。就是说，有可能训练带有小的，部分注释的图像数据集的深度模型。此外，我们已经证明，在处理小型数据集时，我们的AutoML方法在准确性和速度方面均优于其他AutoML工具。