Abstract: Chest disease is one of the most common diseases,chest X-ray is an important method for examination and diagnosis of chest disease.The construction of a highly accurate prediction model for chest diagnosis usually requires a large number of tags and manual annotation of abnormal locations.However,it is difficult to obtain such annotated data,especially data with location annotation.Therefore,building a method that use only a few location annotations becomes a major problem.Although there have been some weak supervision methods to solve this problem,most of them only focus on the image information,rarely considering the relationship between the images.Therefore,a chest X-ray disease diagnosis and location model based on weak supervised deep learning is proposed.While deep learning is used to extract the image information,the graph structure is introduced and hash code is used to add the similarity of the image itself and the relational information of the image into the learning process.In the case of no additional annotation,a small amount of annotation can achieve a good recognition and location effect.After validation on the Chest X-ray dataset,the location accuracy (IoU) is 44% when using only 3% of the location-tagged data.This indicates that this method can effectively identify and locate chest X-ray lesions,provide doctors with candidate areas for screening,and assist doctors in the diagnosis of chest diseases.

Key words: Chest X-ray, Convolutional neural network, Deep learning, Disease diagnosis and localization, Weak supervision