Information Engineering College, Guangzhou Panyu Polytechnic, Guangzhou 511483, China.
School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Biomed Res Int. 2022 Aug 21;2022:8007713. doi: 10.1155/2022/8007713. eCollection 2022.
Applying machine learning technology to automatic image analysis and auxiliary diagnosis of whole slide image (WSI) may help to improve the efficiency, objectivity, and consistency of pathological diagnosis. Due to its extremely high resolution, it is still a great challenge to directly process WSI through deep neural networks. In this paper, we propose a novel model for the task of classification of WSIs. The model is composed of two parts. The first part is a self-supervised encoding network with a UNet-like architecture. Each patch from a WSI is encoded as a compressed latent representation. These features are placed according to their corresponding patch's original location in WSI, forming a feature cube. The second part is a classification network fused by 4 famous network blocks with heterogeneous architectures, with feature cube as input. Our model effectively expresses the feature and preserves location information of each patch. The fused network integrates heterogeneous features generated by different networks which yields robust classification results. The model is evaluated on two public datasets with comparison to baseline models. The evaluation results show the effectiveness of the proposed model.
应用机器学习技术于全切片图像（WSI）的自动图像分析和辅助诊断，可能有助于提高病理诊断的效率、客观性和一致性。由于其极高的分辨率，通过深度神经网络直接处理 WSI 仍然是一个巨大的挑战。在本文中，我们提出了一种用于 WSI 分类任务的新型模型。该模型由两部分组成。第一部分是具有 UNet 结构的自监督编码网络。WSI 的每个斑块都被编码为一个压缩的潜在表示。这些特征根据它们在 WSI 中的原始位置进行放置，形成一个特征立方体。第二部分是一个由 4 个具有异构架构的著名网络块融合而成的分类网络，以特征立方体作为输入。我们的模型有效地表达了每个斑块的特征并保留了位置信息。融合网络集成了不同网络生成的异构特征，从而产生了稳健的分类结果。该模型在两个公开数据集上进行了评估，并与基线模型进行了比较。评估结果表明了所提出模型的有效性。
