Internal medicine

Pathology

Pattern recognition (psychology)

Contextual image classification

Margin (machine learning)

Image (mathematics)

Cancer

Machine learning

Artificial intelligence

Region of interest

Computer-aided diagnosis

Computer science

Breast cancer

Artificial neural network

Histopathology

Medicine

Convolutional neural network

IEEE Transactions on Medical Imaging

An attention guided convolutional neural network (CNN) for the classification of breast cancer histopathology images is proposed. Neural networks are generally applied as black box models and often the network's decisions are difficult to interpret. Making the decision process transparent, and hence reliable is important for a computer-assisted diagnosis (CAD) system. Moreover, it is crucial that the network's decision be based on histopathological features that are in agreement with a human expert. To this end, we propose to use additional region-level supervision for the classification of breast cancer histopathology images using CNN, where the regions of interest (RoI) are localized and used to guide the attention of the classification network simultaneously. The proposed supervised attention mechanism specifically activates neurons in diagnostically relevant regions while suppressing activations in irrelevant and noisy areas. The class activation maps generated by the proposed method correlate well with the expectations of an expert pathologist. Moreover, the proposed method surpasses the state-of-the-art on the BACH microscopy test dataset (part A) with a significant margin.

Guided Soft Attention Network for Classification of Breast Cancer Histopathology Images