Classification of focal liver lesions in CT images using convolutional neural networks with lesion information augmented patches and synthetic data augmentation.

Journal: Medical physics
Published Date: Aug 4, 2021

Abstract

PURPOSE: We propose a deep learning method that classifies focal liver lesions (FLLs) into cysts, hemangiomas, and metastases from portal phase abdominal CT images. We propose a synthetic data augmentation process to alleviate the class imbalance and the Lesion INformation Augmented (LINA) patch to improve the learning efficiency.

Authors

  • Hansang Lee
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea.
  • Haeil Lee
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Helen Hong
    Department of Software Convergence, College of Interdisciplinary Studies for Emerging Industries, Seoul Women's University, 621 Hwarang-ro, Nowon-gu, Seoul, 01797, Korea.
  • Heejin Bae
    Department of Surgery, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Joon Seok Lim
    Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
  • Junmo Kim
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea.

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