Abstract
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.
Highlights
In December 2012, a study [1] showed that musculoskeletal diseases, such as arthritis and back pain, are the secondleading cause of disability as well as the fourth-leading factor to the health of the world population, affecting more than 1.7 billion people worldwide
Aiming at the problems above, this paper proposes an Adversarial Lesion Enhancement Neural Network for Medical Image Classification (ALENN). e method includes two main modules: a structure-based lesion adversarial inpainting module and a classification module based on lesion information fusion. e overall two-stage structure diagram is shown in Figure 1. e LE represents the lesion enhancement
It contains a total of X training sets and X verification sets. e dataset selected 40,561 multi-view X-rays images of 12,173 patients from Stanford Hospital from 2001 to 2012 as sample data and was marked as normal or abnormal by professional radiologists. e result showed that 62% of the images were normal data while 38% were abnormal ones. e dataset consists of research types of finger, elbow, hand, humerus, forearm shoulder, and wrist. is paper only uses elbow data for experimental verification
Summary
In December 2012, a study [1] showed that musculoskeletal diseases, such as arthritis and back pain, are the secondleading cause of disability as well as the fourth-leading factor to the health of the world population, affecting more than 1.7 billion people worldwide. According to data from the World Health Organization [2], there are more than 150 diseases caused by the musculoskeletal (exercise) system. E abnormalities of musculoskeletal are mainly reflected in the basic diseases of bones, joints and soft tissues [3, 4]. Basic joint diseases include swelling of joint, destruction of joint, degeneration of joint, ankylosis of joint, dislocation of joint, etc. Basic soft tissue diseases include soft tissue swelling, soft tissue mass, myophagism, etc
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