Comparison of CNNs and Transformer Models in Diagnosing Bone Metastases in Bone Scans Using Grad-CAM.

Journal: Clinical nuclear medicine
Published Date: Apr 16, 2025

Abstract

PURPOSE: Convolutional neural networks (CNNs) have been studied for detecting bone metastases on bone scans; however, the application of ConvNeXt and transformer models has not yet been explored. This study aims to evaluate the performance of various deep learning models, including the ConvNeXt and transformer models, in diagnosing metastatic lesions from bone scans.

Authors

  • Sehyun Pak
    Department of Medicine, Hallym University College of Medicine, Chuncheon, Gangwon, Republic of Korea.
  • Hye Joo Son
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
  • Dongwoo Kim
    From the Department of Nuclear Medicine, Yonsei University College of Medicine.
  • Ji Young Woo
    Department of Radiology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea.
  • Ik Yang
    Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.
  • Hee Sung Hwang
    Department of Nuclear Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Gyeonggi, Republic of Korea.
  • Dohyoung Rim
    Department of Cognitive Science, Yonsei University, Seoul, Korea.
  • Min Seok Choi
    PE Data Solution, SK hynix, Icheon, Gyeonggi, Republic of Korea.
  • Suk Hyun Lee
    Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.

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