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Scalable histopathological image analysis via supervised hashing with multiple features

机译：通过具有多个功能的监督哈希可扩展的组织病理学图像分析

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摘要

Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Especially, with the rapid growth of digital histopathology, hashing-based retrieval approaches are gaining popularity due to their exceptional efficiency and scalability. Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance. In response, we exploit joint kernel-based supervised hashing (JKSH) to integrate complementary features in a hashing framework. Specifically, hashing functions are designed based on linearly combined kernel functions associated with individual features. Supervised information is incorporated to bridge the semantic gap between low-level features and high-level diagnosis. An alternating optimization method is utilized to learn the kernel combination and hashing functions. The obtained hashing functions compress multiple high-dimensional features into tens of binary bits, enabling fast retrieval from a large database. Our approach is extensively validated on 3121 breast-tissue histopathological images by distinguishing between actionable and benign cases. It achieves 88.1% retrieval precision and 91.3% classification accuracy within 16.5 ms query time, comparing favorably with traditional methods. (C) 2016 Elsevier B.V. All rights reserved.
机译：组织病理学对于癌症的诊断至关重要，但其解释既繁琐又具有挑战性。为了促进此过程，已经开发了基于内容的图像检索方法作为基于案例的推理工具。尤其是，随着数字组织病理学的快速发展，基于散列的检索方法因其出色的效率和可伸缩性而越来越受欢迎。然而，尽管这是提高图像检索性能的普遍做法，但很少有基于散列的组织病理学图像分析方法进行特征融合。作为响应，我们利用基于内核的联合监督哈希（JKSH）将互补功能集成到哈希框架中。具体而言，基于与各个特征相关联的线性组合的内核函数来设计哈希函数。纳入了监督信息以弥合低级功能和高级诊断之间的语义鸿沟。利用一种交替优化方法来学习内核组合和哈希函数。获得的散列函数将多个高维特征压缩为数十个二进制位，从而能够从大型数据库中快速检索。通过区分可操作性病例和良性病例，我们的方法在3121例乳腺组织病理学图像上得到了广泛验证。与传统方法相比，它在16.5 ms的查询时间内可达到88.1％的检索精度和91.3％的分类精度。 （C）2016 Elsevier B.V.保留所有权利。

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