Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection
Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection
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DOI：
10.1109/tmi.2019.2891305
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发表时间：
2019-08-01
影响因子：
10.6
通讯作者：
Pheng-Ann Heng
中科院分区：
文献类型：
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作者：
Lin, Huangjing;Chen, Hao;Pheng-Ann Heng
淋巴结转移是乳腺癌诊断中最重要的指标之一，传统上由病理学家在显微镜下观察。近年来，随着高通量扫描和深度学习技术的飞速发展，从全切片图像中自动分析组织学在医学图像计算领域引起了广泛的兴趣，其目的是减轻病理学家的工作量，同时降低误诊率。然而，从全载玻片图像自动检测淋巴结转移仍然是一个关键挑战，因为这样的图像通常非常大，其中它们通常可以是几个千兆字节的大小。此外，硬模拟物的存在可能导致大量的假阳性。在本文中，我们提出了一种新的方法与锚层模型转换，它不仅利用了全卷积架构的效率，以满足临床实践中的速度要求，而且还密集扫描整个载玻片图像，以实现对微观和宏观转移的准确预测。通过采用异步样本预取和硬否定挖掘策略，可以有效地训练网络。我们的方法的有效性在2016年Camelyon Grand Challenge的基准数据集上得到了证实。与最先进的方法相比，我们的方法在肿瘤定位准确性方面取得了显着的改进，速度更快，甚至在两项挑战任务上都超过了人类的表现。
Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole-slide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists' workload and simultaneously reduce misdiagnosis rate. However, the automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size. Also, the presence of hard mimics may result in a large number of false positives. In this paper, we propose a novel method with anchor layers for model conversion, which not only leverages the efficiency of fully convolutional architectures to meet the speed requirement in clinical practice but also densely scans the whole-slide image to achieve accurate predictions on both micro- and macro-metastases. Incorporating the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. The efficacy of our method is corroborated on the benchmark dataset of 2016 Camelyon Grand Challenge. Our method achieved significant improvements in comparison with the state-of-the-art methods on tumor localization accuracy with a much faster speed and even surpassed human performance on both challenge tasks.