Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China.
Int J Comput Assist Radiol Surg. 2018 Feb;13(2):179-191. doi: 10.1007/s11548-017-1663-9. Epub 2017 Aug 31.
Cell nuclei classification in breast cancer histopathology images plays an important role in effective diagnose since breast cancer can often be characterized by its expression in cell nuclei. However, due to the small and variant sizes of cell nuclei, and heavy noise in histopathology images, traditional machine learning methods cannot achieve desirable recognition accuracy. To address this challenge, this paper aims to present a novel deep neural network which performs representation learning and cell nuclei recognition in an end-to-end manner.
The proposed model hierarchically maps raw medical images into a latent space in which robustness is achieved by employing a stacked denoising autoencoder. A supervised classifier is further developed to improve the discrimination of the model by maximizing inter-subject separability in the latent space. The proposed method involves a cascade model which jointly learns a set of nonlinear mappings and a classifier from the given raw medical images. Such an on-the-shelf learning strategy makes obtaining discriminative features possible, thus leading to better recognition performance.
Extensive experiments with benign and malignant breast cancer datasets are conducted to verify the effectiveness of the proposed method. Better performance was obtained when compared with other feature extraction methods, and higher recognition rate was achieved when compared with other seven classification methods.
We propose an end-to-end DNN model for cell nuclei and non-nuclei classification of histopathology images. It demonstrates that the proposed method can achieve promising performance in cell nuclei classification, and the proposed method is suitable for the cell nuclei classification task.
乳腺癌组织病理学图像中的细胞核分类在有效诊断中起着重要作用，因为乳腺癌通常可以通过其在细胞核中的表达来表征。然而，由于细胞核的体积小且变化大，以及组织病理学图像中的噪声大，传统的机器学习方法无法达到理想的识别精度。针对这一挑战，本文旨在提出一种新的端到端的深度神经网络，该网络可以进行表示学习和细胞核识别。
所提出的模型分层将原始医学图像映射到一个潜在空间中，通过使用堆叠去噪自动编码器来实现稳健性。进一步开发了一个监督分类器，通过最大化潜在空间中的受试者间可分离性来提高模型的判别能力。该方法涉及一个级联模型，该模型联合从给定的原始医学图像中学习一组非线性映射和一个分类器。这种现成的学习策略使得获得判别特征成为可能，从而实现更好的识别性能。
在良性和恶性乳腺癌数据集上进行了广泛的实验，以验证所提出方法的有效性。与其他特征提取方法相比，该方法获得了更好的性能，与其他七种分类方法相比，该方法实现了更高的识别率。
我们提出了一种用于组织病理学图像中细胞核和非细胞核分类的端到端 DNN 模型。它表明，所提出的方法可以在细胞核分类中取得有前途的性能，并且该方法适用于细胞核分类任务。
