Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3518-3521. doi: 10.1109/EMBC46164.2021.9630824.
We proposed a novel model that integrates the fuzzy theory and group equivariant convolutional neural network for histopathologic cancer detection. The proposed fuzzy group equivariant convolutional neural network consists of the convolutional network, a novel fuzzy global pooling layer, and a fully connected network. In the fuzzy global pooling layer, the generated feature maps are transferred into the fuzzy domain by two different fuzzification methods. One of the fuzzy feature maps exploits the uncertainty information of histopathologic images, and the other keeps the original information. Furthermore, the fuzzy feature maps are processed by using Min-max operations. The experiments verified that the proposed method could always find the maximum fuzzy entropy and exploit and present the uncertainty of histopathologic images well. The experiments using the benchmark dataset demonstrate that the proposed model becomes more accurate and outperforms the existing models including the benchmark models. Compared to the benchmark model with 89.8% of accuracy, 96.3% of AUC, and 0.260 of negative log-likelihood loss, the proposed model obtained 91.7% of accuracy, 97.2% of AUC, and 0.214 of negative log-likelihood loss.
我们提出了一种新颖的模型，该模型将模糊理论和群组等变卷积神经网络相结合，用于组织病理学癌症检测。所提出的模糊群组等变卷积神经网络由卷积网络、新颖的模糊全局池化层和全连接网络组成。在模糊全局池化层中，通过两种不同的模糊化方法将生成的特征图转换到模糊域中。模糊特征图之一利用了组织病理学图像的不确定性信息，而另一个则保留了原始信息。此外，模糊特征图通过 Min-max 操作进行处理。实验验证了所提出的方法始终能够找到最大模糊熵，并很好地利用和呈现组织病理学图像的不确定性。使用基准数据集的实验表明，所提出的模型比包括基准模型在内的现有模型更加准确和优越。与具有 89.8%准确率、96.3%AUC 和 0.260 负对数似然损失的基准模型相比，所提出的模型获得了 91.7%的准确率、97.2%AUC 和 0.214 的负对数似然损失。
