Sequential Modeling of Deep Features for Breast Cancer Histopathological Image Classification

Computerized approaches for automated classification of histopathology images can help in reducing the manual observational workload of pathologists. In recent years, like in other areas, deep networks have also attracted attention for histopathology image analysis. However, existing approaches have paid little attention in exploring multi-layer features for improving the classification. We believe that considering multi-layered features is important as different regions in the images, which are in turn at different magnifications may contain useful discriminative information at different levels of hierarchy. Considering the dependency exists among the layers in deep learning, we propose sequential framework which utilizes multi-layered deep features that are extracted from fine-tuned DenseNet. A decision is made by layer for a sample only if it passes a pre-defined cut-off confidence for that layer otherwise, the sample is passed on to next layers. Various experiments on publicly available BreaKHis dataset, demonstrate the proposed framework yields better performance, in most cases, than typically used highest layer features. We also compare results with the framework where each layer is treated independently. This indicates that low-mid-level features also carry useful discriminative information, when explicitly considered. We also demonstrate an improved performance over various state-of-the-art methods.