A novel biomedical image indexing and retrieval system via deep preference learning. (May 2018) - Pang, Shuchao; Orgun, Mehmet A.; Yu, Zhezhou
Highlights: This work provides insights into, and gives a detailed description of, how the proposed biomedical image indexing system leverages deep learning and preference learning. Based on our proposed system and algorithms, we investigate how to transfer the learnt deep knowledge from another domain into biomedical image processing area with two kinds of well-known pre-trained deep neural networks, which sheds light on how we can deal with the lack of annotated biomedical images in the target domain. We conduct extensive experiments with three public available databases of different levels of difficulty to study whether the proposed framework can exhibit stable performance over biomedical databases with images of different formats and types. This paper provides extensive analysis to contrast the proposed framework with those of popular biomedical image indexing approaches and previous regular image retrieval methods in terms of feature comparison, time complexity, system compatibility etc. Furthermore, we discuss the robustness of deep features in detail. Our work tables a proposal for an innovative application of the proposed framework in an integrated biomedical image retrieval system. Abstract: Background and Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.