论文标题
使用卷积神经网络进行癌症检测的染色标准化乳房组织病理学图像识别
Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection
论文作者
论文摘要
数字病理学中的计算机辅助诊断正变得无处不在，因为它可以提供更有效，客观的医疗诊断。最近的进步表明，卷积神经网络（CNN）体系结构是一种已建立的深度学习范式，可用于设计计算机辅助诊断（CAD）系统以进行乳腺癌检测。但是，由于污渍的可变性和这种深度学习框架的污渍归一化的影响，尚待探索污渍的效果。 Moreover, performance analysis with arguably more efficient network models, which may be important for high throughput screening, is also not well explored.To address this challenge, we consider some contemporary CNN models for binary classification of breast histopathology images that involves (1) the data preprocessing with stain normalized images using an adaptive colour deconvolution (ACD) based color normalization algorithm to handle the stain variabilities; （2）应用一些基于转移学习的培训，可以说是一些更有效的CNN模型，即视觉几何组网络（VGG16），Mobilenet和EfficityNet。我们已经在公开可用的Breakhis数据集上验证了经过培训的CNN网络，用于200倍和400倍放大的组织病理学图像。实验分析表明，在大多数情况下，经过预定的网络在数据增强的乳房组织病理学图像上产生更好的质量结果，而不是没有染色归一化的情况。此外，我们使用染色标准化图像评估了流行的轻型网络的性能和效率，并发现有效网络在测试准确性和F1得分方面优于VGG16和Mobilenet。我们观察到，在测试时间方面的效率在有效网络中比其他网络更好。 VGG网络Mobilenet，分类精度没有太大下降。
Computer assisted diagnosis in digital pathology is becoming ubiquitous as it can provide more efficient and objective healthcare diagnostics. Recent advances have shown that the convolutional Neural Network (CNN) architectures, a well-established deep learning paradigm, can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. However, the challenges due to stain variability and the effect of stain normalization with such deep learning frameworks are yet to be well explored. Moreover, performance analysis with arguably more efficient network models, which may be important for high throughput screening, is also not well explored.To address this challenge, we consider some contemporary CNN models for binary classification of breast histopathology images that involves (1) the data preprocessing with stain normalized images using an adaptive colour deconvolution (ACD) based color normalization algorithm to handle the stain variabilities; and (2) applying transfer learning based training of some arguably more efficient CNN models, namely Visual Geometry Group Network (VGG16), MobileNet and EfficientNet. We have validated the trained CNN networks on a publicly available BreaKHis dataset, for 200x and 400x magnified histopathology images. The experimental analysis shows that pretrained networks in most cases yield better quality results on data augmented breast histopathology images with stain normalization, than the case without stain normalization. Further, we evaluated the performance and efficiency of popular lightweight networks using stain normalized images and found that EfficientNet outperforms VGG16 and MobileNet in terms of test accuracy and F1 Score. We observed that efficiency in terms of test time is better in EfficientNet than other networks; VGG Net, MobileNet, without much drop in the classification accuracy.
