SN-FPN: Self-Attention Nested Feature Pyramid Network for Digital Pathology Image Segmentation

Abstract

Digital pathology has played a key role in replacing glass slides with digital images, enhancing various pathology workflows. Whole slide images are digitized pathological images improving the capabilities of digital pathology and contributing to the overall turnaround time for diagnoses. The digitized images have been successfully integrated with artificial intelligence algorithms assisting pathologists in many tasks, but there are still demands to develop a new algorithm for a better diagnosis process. In this paper, we propose a new deep convolutional neural network model integrating a feature pyramid network with a self-attention mechanism in three pathways: encoder, decoder, and self-attention nested for providing accurate tumor region segmentation on whole slide images. The encoder pathway adopts ResNet50 architecture for the bottom-up network. The decoder pathway adopts the feature pyramid network for the top-down network. The self-attention nested pathway forms the attention map represented by the distribution of attention scores focusing on localizing tumor regions and avoiding irrelevant information. The results of our experiment show that the proposed model outperforms the state-of-the-art deep convolutional neural network models in terms of tumor and stromal region segmentation. Moreover, various encoder networks were equipped with the proposed model and compared with each other. The results indicate that the ResNet series using the proposed model outperforms other encoder networks.

Keywords

• Digital pathology images
• deep learning
• encoder
• decoder
• image segmentation