As digital medical imaging becomes more prevalent and archives increase in size, representation learning exposes an interesting opportunity for enhanced medical decision support systems. On the other hand, medical imaging data is often scarce and short on annotations. In this paper, we present an assessment of unsupervised feature learning approaches for images in the biomedical literature, which can be applied to automatic biomedical concept detection. Six unsupervised representation learning methods were built, including traditional bags of visual words, autoencoders, and generative adversarial networks. Each model was trained, and their respective feature space evaluated using images from the ImageCLEF 2017 concept detection task. We conclude that it is possible to obtain more powerful representations with modern deep learning approaches, in contrast with previously popular computer vision methods. Although generative adversarial networks can provide good results, they are harder to succeed in highly varied data sets. The possibility of semi-supervised learning, as well as their use in medical information retrieval problems, are the next steps to be strongly considered.
翻译:随着数字医学成像越来越普遍,档案的数量增加,代表性学习暴露了加强医疗决策支持系统的一个有趣机会。另一方面,医学成像数据往往很少,说明也很短。在本文件中,我们评估了生物医学文献中未受监督的特征学习方法,可用于生物医学概念的自动检测。建立了六种不受监督的代表性学习方法,包括传统的视觉文字袋、自动识别器和基因对抗网络。每个模型都接受了培训,利用2017年图像CLEF概念探测任务中的图像对各自的特征空间进行了评估。我们的结论是,与以前流行的计算机视觉方法相比,现代深层学习方法有可能获得更强有力的体现。尽管基因对抗网络可以提供良好的结果,但在高度多样化的数据集中,它们更难取得成功。接下来要认真考虑的是半监督学习的可能性,以及它们在医疗信息检索问题中的使用。