Deep adversarial model for musculoskeletal quality evaluation. Issue 1 (January 2020)

• Record Type: Journal Article
• Authors: Li, Shenglong
• Is Part Of: Information processing & management. Volume 57:Issue 1(2020:Jan.)
• Publication Date: 2020-01
• Subjects: Health evaluation -- Deep neural network -- Musculoskeletal abnormal -- Adversarial learning; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2019.102146
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We proposed a dilated dense convolutional network to automatically extract important visual features in radiographic images. We introduce adversarial learning techniques to improve the model performance in real time. The overall framework can be jointly trained in an end-to-end fashion. We develop a loss function that considers influences from both supervised and unsupervised processes. Abstract: Radiographic images are commonly used in medical imaging techniques. Interpretation and diagnosis of radiographic images are essential for the treatment of disease. However, it is a time-consuming task for radiologists to interpret a large number of radiological images, so it is significant to develop deep learning techniques to evaluate abnormal parts in radiographic images automatically. With the releasing of the musculoskeletal X-ray image dataset MURA, the evaluation of skeletal muscle abnormal sites in radiographic images has received increasing attention. In this paper, we propose a deep neural network based method for evaluating musculoskeletal quality and finding abnormal sites in radiographic images. We develop a deep dilated convolutional neural network (CNN) for automatic learning of visual features that are highly related to musculoskeletal quality. Based on the quality evaluation results, the model is able to locate abnormal sites. To improve the performance of the method, we introduce an adversarial learning based model to guide its training process iteratively. We test the performance of the proposed method on the standard dataset for musculoskeletal abnormal evaluation. Experimental results are compared with state-of-the-art methods, showing that the proposed method exhibits impressive performance on all of the test classes.