「Google AI Blog」Note on Applying Deep Learning to Metastatic Breast Cancer Detection

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谷歌人工智能博客博文：Applying Deep Learning to Metastatic Breast Cancer Detection

Posted by Martin Stumpe, Technical Lead and Craig Mermel, Product Manager, Healthcare, Google AI

谷歌不止deepmind搞医学图像/信号，大本营也有部门。

However, studies have shown that about 1 in 4 metastatic lymph node staging classifications would be changed upon second pathologic review, and detection sensitivity of small metastases on individual slides can be as low as 38% when reviewed under time constraints.

受时间限制，小型转移性的检测发现低至38%。

Last year, we described our deep learning–based approach to improve diagnostic accuracy (LYmph Node Assistant, or LYNA) to the 2016 ISBI Camelyon Challenge, which provided gigapixel-sized pathology slides of lymph nodes from breast cancer patients for researchers to develop computer algorithms to detect metastatic cancer. While LYNA achieved significantly higher cancer detection rates (Liu et al. 2017) than had been previously reported, an accurate algorithm alone is insufficient to improve pathologists’ workflow or improve outcomes for breast cancer patients.

2017有篇文章提出LYNA，其癌症检测率比之前的报告的更高。

In “Artificial Intelligence Based Breast Cancer Nodal Metastasis Detection: Insights into the Black Box for Pathologists” (Liu et al. 2018), published in the Archives of Pathology and Laboratory Medicine and “Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer” (Steiner, MacDonald, Liu et al. 2018) published in The American Journal of Surgical Pathology, we present a proof-of-concept pathologist assistance tool based on LYNA, and investigate these factors.

谷歌发表了两篇文章。

量子位有这篇博文的中文翻译：谷歌医疗AI又有新进展：转移性乳腺癌检测准确率达99%。

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在国内，很多医学AI初创公司在病理图像分析上有产品。据我的了解，有视见医疗、柏视医疗、迪英加医疗等。