Gastrointestinal Tract Diseases Detection with Deep Attention Neural Network

Yuan Chang, Zixuan Huang, Weizhao Chen, Qiwei Shen

Abstract: Medical image classification and diagnosis is currently a hot topic in the field of deep learning. The ACM International Conference on Multimedia and Simula co-hosted the MutilMedia Grand Challenge, which aims to use artificial intelligence aiding detection and classification of gastrointestinal image. This competition is divided into four subtasks, including detection, efficient detection, efficient detection (the same hardware for all the participants) and report generation. We participate in the multi-label detection task and propose a new attention model, which can effectively improve the network's ability to classify different types of categories. Our approach also uses a series of different techniques including multi-epoch fusion, automatic data augmentation selection, and adaptive threshold selection. Combining these techniques, we are able to achieve good classification results on the given dataset. Finally, our f1 score is 0.907 and MCC is 0.952 with a high speed.