期刊
APPLIED SCIENCES-BASEL
卷 8, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/app8081213
关键词
representation learning; unsupervised learning; deep learning; content-based image retrieval
类别
资金
- ERDF (European Regional Development Fund) through the Operational Programme for Competitiveness and Internationalisation-COMPETE 2020 Programme
- FCT-Fundacao para a Ciencia e a Tecnologia [PTDC/EEI-ESS/6815/2014]
- FCT [PD/BD/105806/2014]
- Fundação para a Ciência e a Tecnologia [PTDC/EEI-ESS/6815/2014, PD/BD/105806/2014] Funding Source: FCT
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 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 spaces evaluated using images from the ImageCLEF 2017 concept detection task. The highest mean F-1 score of 0.108 was obtained using representations from an adversarial autoencoder, which increased to 0.111 when combined with the representations from the sparse denoising autoencoder. We conclude that it is possible to obtain more powerful representations with modern deep learning approaches than with previously popular computer vision methods. The possibility of semi-supervised learning as well as its use in medical information retrieval problems are the next steps to be strongly considered.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据