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首页> 外文期刊>IEEE transactions on multimedia >Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review
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Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review

机译：大型多模式医学数据集的检索和理解：综述

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Content-based multimedia retrieval (CBMR) has been an active research domain since the mid 1990s. In medicine visual retrieval started later and has mostly remained a research instrument and less a clinical tool. The limited size of data sets due to privacy constraints is often mentioned as reason for these limitations. Nevertheless, much work has been done in CBMR, including the availability of increasingly large data sets and scientific challenges. Annotated data sets and clinical data for images have now become available and can be combined for multi-modal retrieval. Much has been learned on user behavior and application scenarios. This text is motivated by the advances in medical image analysis and the availability of public large data sets that often include clinical data. It is a systematic review of recent work (concentrating on the period 2011–2017) on multi-modal CBMR and image understanding in the medical domain, where image understanding includes techniques such as detection, localization, and classification for leveraging visual content. With the objective of summarizing the current state of research for multimedia researchers outside the medical field, the text provides ways to get data sets and identifies current limitations and promising research directions. The text highlights advances in the past six years and a trend to use larger scale training data and deep learning approaches that can replace/complement hand-crafted features. Using images alone will likely only work in limited domains but combining multiple sources of data for multi-modal retrieval has the biggest chances of success, particularly for clinical impact.
机译：自1990年代中期以来，基于内容的多媒体检索（CBMR）一直是活跃的研究领域。在医学上，视觉检索开始较晚，并且在很大程度上仍然是一种研究工具，而少了一种临床工具。通常会提到由于隐私限制而导致的数据集大小有限，这是这些限制的原因。尽管如此，CBMR已经完成了许多工作，包括越来越大的数据集的可用性和科学挑战。图像的带注释的数据集和临床数据现已可用，并且可以组合用于多模式检索。关于用户行为和应用场景，已经学到了很多东西。本文的动机是医学图像分析的发展以及公共大型数据集（通常包括临床数据）的可用性。它是对医学领域中多模式CBMR和图像理解的最新工作（集中于2011-2017年）的系统评价，其中图像理解包括检测，定位和分类等技术，以利用视觉内容。为了总结医学领域以外的多媒体研究人员的研究现状，本文提供了获取数据集的方法，并确定了当前的局限性和有希望的研究方向。本书重点介绍了过去六年中取得的进步，以及使用较大规模的训练数据和深度学习方法（可以替代/补充手工制作的功能）的趋势。仅使用图像可能仅在有限的领域内有效，但结合多种数据源进行多模式检索具有最大的成功机会，尤其是对于临床影响而言。

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