Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model.
Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model.
复制标题
DOI：
10.1038/s41598-017-04075-z
复制
发表时间：
2017-06-23
影响因子：
4.6
通讯作者：
Li S
中科院分区：
文献类型：
--
作者：
Han Z;Wei B;Zheng Y;Yin Y;Li K;Li S
从组织病理学图像中自动进行乳腺癌多分类在计算机辅助乳腺癌诊断或预后中起着关键作用。乳腺癌多分类是指确定乳腺癌(导管癌、纤维腺瘤、小叶癌等)的从属类别。然而，从组织病理学图像进行乳腺癌多分类面临着两个主要挑战：(1)乳腺癌多分类方法与二值分类(良、恶性)相比存在很大困难；(2)由于高分辨率图像外观的广泛变异性、癌细胞的高相干性和颜色分布的广泛不均匀，多类之间存在细微差异。因此，从组织病理学图像中自动对乳腺癌进行多分类具有重要的临床意义，但尚未被探索。现有文献仅关注乳腺癌的二分类，而不支持进一步的乳腺癌定量评估。在这项研究中，我们提出了一种使用新提出的深度学习模型的乳腺癌多分类方法。结构化深度学习模型在大规模数据集上取得了显著的性能(平均93.2%的准确率)，这表明我们的方法在为临床环境下的乳腺癌多分类提供了一种有效的工具。
Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.