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Abstract：
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.
摘要：
医疗领域诊断技术的快速发展对医师处理和整合异构、但在常规练习中产生的补充数据。例如,单个癌症患者的个性化诊断和治疗计划依赖于各种图像(例如放射学，病理学和相机图像）和非图像数据（例如临床数据和基因组数据）。然而,这种决策程序可能是主观的，定性，并且具有很大的主体间变异性。随着多模式深度学习技术的最新进展，越来越多的努力已经致力于一个关键问题：我们如何提取和聚合多模态信息，最终提供更客观，定量计算机辅助临床决策？本文回顾了有关处理此类问题的最新研究。简而言之，这篇综述将包括(A)当前多模式学习工作流程的概述，(B)多模态融合方法的总结，(c)关于业绩的讨论，(d)在疾病诊断和预后中的应用，(e)挑战和未来方向。
