Large-scale retrieval for medical image analytics: A comprehensive review

Abstract

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis.

Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis

“…Representation learning has been notably used in medical image retrieval, although even in this decade, handcrafted visual feature extraction algorithms are frequently considered in this context [11], [12]. Nonetheless, although the interest in deep learning is relatively recent, a wide variety of neural networks have been studied for medical image analysis [13], as they often exhibit greater potential for the task [14].…”

Artificial bee colony algorithm for content‐based image retrieval

“…A large number of these image collections pose increasing technical challenges for computer systems in order to manage image data effectively and to make such collections readily available. Many programs and tools have been developed to formulate and execute queries based on visual content and to facilitate searching through large image repositories …”

“…Many programs and tools have been developed to formulate and execute queries based on visual content and to facilitate searching through large image repositories. [1][2][3][4][5][6] Methods of image retrieval can be categorized into two approaches, 7 which are the description-based and content-based. In the description-based image retrieval approach, the retrieval is based on utilizing various methods of adding metadata to the images, such as captions, keywords, or descriptions, so that the retrieval can be performed based on these types of annotated information, whereas the content-based image retrieval (CBIR) approach differs from the description-based approach, in which the search method used in the CBIR approach analyzes the contents of the image rather than the metadata.…”

Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images

“…An extensive body of prior work around content-based image retrieval (CBIR) is perhaps the most relevant toward providing classification decisions with evidence [9,10,11,12,13]. Early approaches relied on low-level features and bag-ofvisual words, [9,10,11], but suffered from the "semantic gap": feature similarity did not necessarily correlate to label similarity.…”