Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network

• Published in: IEEE transactions on big data Vol. 7; no. 4; pp. 750 - 758
• Main Authors: Chen, Min, Shi, Xiaobo, Zhang, Yin, Wu, Di, Guizani, Mohsen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Biomedical imaging; Computed tomography; Convolutional autoencoder neural network; Convolutional codes; Deep learning; Diagnosis; Feature extraction; feature learning; hand-craft feature; Image analysis; lung nodule; Lungs; Medical imaging; Neural networks; Nodules; Training; unsupervised learning
• ISSN: 2332-7790; 2372-2096
• DOI: 10.1109/TBDATA.2017.2717439

At present, computed tomography (CT) is widely used to assist disease diagnosis. Especially, computer aided diagnosis (CAD) based on artificial intelligence (AI) recently exhibits its importance in intelligent healthcare. However, it is a great challenge to establish an adequate labeled dataset for CT analysis assistance, due to the privacy and security issues. Therefore, this paper proposes a convolutional autoencoder deep learning framework to support unsupervised image features learning for lung nodule through unlabeled data, which only needs a small amount of labeled data for efficient feature learning. Through comprehensive experiments, it shows that the proposed scheme is superior to other approaches, which effectively solves the intrinsic labor-intensive problem during artificial image labeling. Moreover, it verifies that the proposed convolutional autoencoder approach can be extended for similarity measurement of lung nodules images. Especially, the features extracted through unsupervised learning are also applicable in other related scenarios.