Autoencoding the retrieval relevance of medical images

By Zehra Camlica, Hamid R. Tizhoosh, Farzad Khalvati. 2015

41 citations recorded by the source corpus.

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a n/p/n autoencoder (p <; n). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Relational autoencoder for feature extraction (2017, background): “Although subsequent applications [27–32] conﬁrm that considering data relationship can improve the performance of autoencoder in dimensionality reduction, the Generalized Autoencoder model has some drawbacks.”
Medical image retrieval using deep convolutional neural network (2017, background): “Local Binary Patterns, Support Vector Machine, and Auto Encoder [48] 14410 12677 1733 X-Ray 57 0.”
Medical Image Analysis using Convolutional Neural Networks: A Review (2017, background): “Machine learning plays a vital role in CADx with its applications in medical image analysis, tissue or cancer detection and classification, image guided therapy, medical image annotation and medical image retrieval [9-13].”