College of Computer Science and Technology, Jilin University, Qianjin Street: 2699, Jilin Province, China; Department of Computing, Macquarie University, Sydney, NSW 2109, Australia.
Department of Computing, Macquarie University, Sydney, NSW 2109, Australia.
Comput Methods Programs Biomed. 2018 May;158:53-69. doi: 10.1016/j.cmpb.2018.02.003. Epub 2018 Feb 6.
The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images.
We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval.
We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images.
We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications.
传统的生物医学图像检索方法和最初为非生物医学图像设计的基于内容的图像检索 (CBIR) 方法，要么只考虑使用像素和低级特征来描述图像，要么使用深度学习特征来描述图像，但在准确性和效率方面仍有很大的改进空间。在这项工作中，我们提出了一种新的方法，利用深度学习技术从生物医学图像中提取高级和紧凑的特征。深度特征提取过程利用多个隐藏层来捕获高分辨率图像的大量特征结构，并在不同的抽象层次上表示它们，从而提高了生物医学图像的索引和检索性能。
我们利用当前流行的多层深度神经网络，即堆叠去噪自动编码器 (SDAE) 和卷积神经网络 (CNN)，通过从另一个领域转移预先训练的深度神经网络的特征表示和参数，来表示生物医学图像的判别特征。此外，为了对所有图像进行索引以找到类似参考的图像，我们还引入了偏好学习技术，为查询图像训练和学习一种偏好模型，该模型可以从生物医学图像数据库输出图像的相似性排名列表。据我们所知，本文首次将偏好学习技术引入生物医学图像检索。
我们评估了基于我们提出的系统的两种强大算法的性能，并将其与流行的生物医学图像索引方法和现有的常规图像检索方法进行了比较，在几个著名的公共生物医学图像数据库上进行了详细的实验。基于不同的检索性能评估标准，实验结果表明，我们提出的算法在生物医学图像索引方面优于最新技术。
我们提出了一种基于深度偏好学习的新型自动索引系统，用于描述生物医学图像，以开发医疗保健中的计算机辅助诊断 (CAD) 系统。我们提出的系统在生物医学图像检索应用中表现出出色的索引能力和高效率，可用于收集和注释生物医学数据库中的高分辨率图像，以进行进一步的生物医学图像研究和应用。
