Deep multi-scale resemblance network for the sub-class differentiation of adrenal masses on computed tomography images.

Journal: Artificial intelligence in medicine
Published Date: Aug 9, 2022

Abstract

OBJECTIVE: The accurate classification of mass lesions in the adrenal glands ('adrenal masses'), detected with computed tomography (CT), is important for diagnosis and patient management. Adrenal masses can be benign or malignant and benign masses have varying prevalence. Classification methods based on convolutional neural networks (CNNs) are the state-of-the-art in maximizing inter-class differences in large medical imaging training datasets. The application of CNNs, to adrenal masses is challenging due to large intra-class variations, large inter-class similarities and imbalanced training data due to the size of the mass lesions.

Authors

  • Lei Bi
  • Jinman Kim
    School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia. Electronic address: jinman.kim@sydney.edu.au.
  • Tingwei Su
    Department of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Michael Fulham
    School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia; Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, Australia; Sydney Medical School, University of Sydney, Australia. Electronic address: michael.fulham@sydney.edu.au.
  • David Dagan Feng
  • Guang Ning
    Department of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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