A Medical Image Classification Model Based on Adversarial Lesion Enhancement

With the development of Artificial Intelligence, the auxiliary diagnosis model based on deep learning can assist doctors to a certain extent. However, the latent information in medical images, such as lesion features, is ignored in most of the traditional methods. The extraction of this information is regarded as a learning task within the network in some recent researches, but it requires a large amount of fine-labeled data, which is undoubtedly expensive. In response to the problem above, this paper proposes an Adversarial Lesion Enhancement Neural Network for Medical Image Classification (ALENN), which is used to locate and enhance the lesion information in medical images only under weakly annotated data so as to improve the accuracy of the auxiliary diagnosis model. This method is a two-stage framework, including a structure-based lesion adversarial inpainting module and a lesion enhancement classification module. The first stage is used to repair the lesion area in the images while the second stage is used to locate the lesion area and use the lesion enhanced data during modeling process. In the end, we verified the effectiveness of our method on the MURA dataset, a musculoskeletal X-ray dataset released by Stanford University. Experimental results show that our method can not only locate the lesion area but also improve the effectiveness of the auxiliary diagnosis model.