Advanced deep learning applications in diagnostic pathology
Advanced deep learning applications in diagnostic pathology
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DOI：
10.33611/trs.2021-005
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
D. Komura;S. Ishikawa
中科院分区：
文献类型：
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作者：
D. Komura;S. Ishikawa
.多年来，病理学家通过在显微镜下观察组织标本来进行组织病理学诊断。然而，最近，已经可以使用专用载玻片扫描仪扫描组织样本的整个载玻片并存储所得到的高分辨率数字图像，即，整个幻灯片图像。这导致了数字病理学的出现，这是一个将整个切片图像用于组织病理学诊断的领域。这一领域正在逐步扩大，特别是在大学医院等大型医院。此外，自2012年以来，图像识别技术取得了巨大的进步，当时深度学习以压倒性的准确性赢得了一般图像识别竞赛ILSVRC。随后，深度学习被应用于各种医学图像，包括X光、眼底和皮肤图像，据报道，在每个领域都达到了通才甚至专业水平的诊断准确率。类似地，针对数字组织病理学图像的深度学习用于辅助病理学诊断正逐渐变得可行，特别是对于许多病例发生的疾病。最近，已经开发了高级应用，例如搜索相似病例，从组织学图像预测基因突变，以及从苏木精和曙红染色图像生成特殊染色图像。这些新兴的应用有可能极大地扩展诊断病理学领域，并有助于医学的进一步发展。在这篇综述中，我们介绍了深度学习技术在该领域的应用，详细介绍了当前的高级应用，并推测了未来的前景。
. For many years, pathologists have performed histopathological diagnoses by observing tissue specimens under a microscope. Recently, however, it has become possible to scan whole slides of tissue specimens using a dedicated slide scanner and store the resultant high-resolution digital images, i.e., whole slide images. This has led to the emergence of digital pathology, a field in which whole slide images are used for histopathological diagnoses. This field is gradually expanding, especially in large hospitals such as university hospitals. In addition, dramatic advancements in image recognition technology have been made since 2012 when deep learning won the general image recognition competition ILSVRC with overwhelming accuracy. Subsequently, deep learning has been applied to various medical images, including X-ray, ocular fundus, and skin images, and is reported to have achieved generalist- or even professional-level diagnostic accuracy in each field. Similarly, the use of deep learning, directed towards digital histopathological images, for assistance with pathological diagnoses is gradually becoming practicable, especially for diseases in which many cases occur. Recently, advanced applications have been developed such as searching for similar cases, predicting genetic mutations from histological images, and generating special stained images from hematoxylin and eosin-stained images. These emerging applications have the potential to greatly expand the field of diagnostic pathology and contribute to the further development of medicine. In this review, we introduce the use of deep learning technology in the field, detail the current advanced applications, and speculate on future perspectives.