Senousy Zakaria, Abdelsamea Mohammed M, Mohamed Mona Mostafa, Gaber Mohamed Medhat
School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7AP, UK.
Faculty of Computers and Information, Assiut University, Assiut 71515, Egypt.
Entropy (Basel). 2021 May 16;23(5):620. doi: 10.3390/e23050620.
Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.
使用深度卷积神经网络（DCNN）的自动分级系统已证明其能够利用数字化组织病理学图像区分不同的乳腺癌分级，并具有这样做的潜力。在数字乳腺病理学中，使用机器置信度指标来衡量DCNN在分级时的置信度至关重要，尤其是在存在诸如图像视觉变异性高之类的主要计算机视觉挑战性问题的情况下。这样的定量指标不仅可以用于提高自动化系统的鲁棒性，还可以帮助医学专业人员识别复杂病例。在本文中，我们提出了用于浸润性乳腺癌显微镜图像分级的基于熵的DCNN模型弹性集成（3E-Net），该模型提供了一个可解释性的初始阶段（使用采用熵的不确定性感知机制）。我们提出的模型设计方式为：（1）排除对我们的集成模型不太敏感且高度不确定的图像；（2）使用集成架构中的特定模型对未排除的图像进行动态分级。我们在一个浸润性乳腺癌数据集上评估了3E-Net的两种变体，分级准确率分别达到了96.15%和99.50%。
