乳腺癌组织病理图像的自动分类可以减轻病理学家的工作负担并提供准确的诊断。然而，其中一个挑战是经验数据集通常是不平衡的，导致相对于基于平衡数据集的传统方法来说分类质量较差。最近提出的双边分支网络（BBN）通过考虑表示学习和分类器学习来解决这个问题，以提高分类性能。我们首先将双边采样策略应用于不平衡的乳腺癌组织病理图像分类，并提出了基于元自适应加权的双边多维精细空间特征注意网络（MAW-BMRSFAN）。该模型由BMRSFAN和MAWN组成。具体而言，精细空间特征注意模块（RSFAM）基于卷积长短期记忆（ConvLSTM）。它旨在提取不同维度的图像分类的精细空间特征，并插入到分类模型的不同层中。同时，提出了MAWN来建模从平衡元数据集到不平衡数据集的映射。它通过直接从少量平衡数据集中自适应学习来更灵活地为BMRSFAN找到合适的加权参数。实验证明，MAW-BMRSFAN的性能优于以前的方法。即使在不平衡因子为16的极端不平衡情况下，MAW-BMRSFAN在四个不同放大倍率下的识别精度仍高于80%，表明MAW-BMRSFAN在极端不平衡的条件下能够达到理想的性能。版权所有 © 2023 Elsevier Ltd. 保留所有权利。

Breast cancer histopathological image automatic classification can reduce pathologists workload and provide accurate diagnosis. However, one challenge is that empirical datasets are usually imbalanced, resulting in poorer classification quality compared with conventional methods based on balanced datasets. The recently proposed bilateral branch network (BBN) tackles this problem through considering both representation and classifier learning to improve classification performance. We firstly apply bilateral sampling strategy to imbalanced breast cancer histopathological image classification and propose a meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network (MAW-BMRSFAN). The model is composed of BMRSFAN and MAWN. Specifically, the refined space feature attention module (RSFAM) is based on convolutional long short-term memories (ConvLSTMs). It is designed to extract refined spatial features of different dimensions for image classification and is inserted into different layers of classification model. Meanwhile, the MAWN is proposed to model the mapping from a balanced meta-dataset to imbalanced dataset. It finds suitable weighting parameter for BMRSFAN more flexibly through adaptively learning from a small amount of balanced dataset directly. The experiments show that MAW-BMRSFAN performs better than previous methods. The recognition accuracy of MAW-BMRSFAN under four different magnifications still is higher than 80% even when unbalance factor is 16, indicating that MAW-BMRSFAN can make ideal performance under extreme imbalanced conditions.Copyright © 2023 Elsevier Ltd. All rights reserved.