Deep manifold feature fusion for classification of breast histopathology images

Histopathology image analysis is the gold standard for breast cancer diagnosis, yet the classification of breast histopathology images is challenging. Convolutional neural networks usually classify images based on deep abstract features only, ignoring the influence of structure information contained in low-level features, which limits the classification ability of deep models. To address the above problem, a breast histopathology image classification method based on the deep manifold fusion of multilayer features is proposed, LPMF2Net. By exploring the complementarity between features at different levels, the multilayer features were cross-cascaded and fused to enhance the cell structure characterization ability. Local preserving projection is applied for the fused features to reduce the interference of redundant information and optimize fusion performance. Moreover, the projection matrix was adaptively adjusted according to the local preserving regularization term to further optimize the model. The proposed LPMF2Net model was tested on the public dataset BreaKHis, and the experimental results (40x: 94.91%, 100x: 96.12%, 200x: 95.51%, 400x: 95.42%) proved its effectiveness. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

In this study, a breast histopathology image classification method based on deep manifold fusion of multilayer features, LPMF2Net, is proposed. By fusing features at different levels and applying local preserving projection and adaptive adjustment of the projection matrix, the model's effectiveness is verified through experimental results.