Overview of the 2014 Workshop on Medical Computer Vision—Algorithms for Big Data (MCV 2014)

Müller, Henning; Menze, Bjoern; Montillo, Albert; Kelm, Michael; Zhang, Shaoting; Cai, Weidong; Metaxas, Dimitris N.

doi:10.1007/978-3-319-13972-2_1

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Lung CT, DX, and CR ADNI (of Southern California) Unknown Alzheimer's MR, PET, etc NBIA (NBIA) 76,000 Cancer Images, Multi-modalities CAMELYON 17 (, DIAG) 1,000, 2TB Whole-slide Histopathological Images PubMed Center (NCBI) 4,000,000 Multi-modalities NLST (Institute, c) 76,000 Lung CT, Pathology Images several challenges, workshops and provided multiple benchmarks related to large-scale data in medical image analysis and retrieval (Langs et al, 2012;Müller et al, 2014;Zhang et al, 2015a).…”

Section: Evaluation Criteriamentioning

confidence: 99%

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

Zhang

Müller

et al. 2018

Medical Image Analysis

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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.

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Section: Evaluation Criteriamentioning

confidence: 99%