한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제25권8호
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  • Pages.1224-1232
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  • 2022
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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CNN 기반 전이학습을 이용한 뼈 전이가 존재하는 뼈 스캔 영상 분류

Classification of Whole Body Bone Scan Image with Bone Metastasis using CNN-based Transfer Learning

  • Yim, Ji Yeong (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Do, Thanh Cong (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Kim, Soo Hyung (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Lee, Guee Sang (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Lee, Min Hee (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Min, Jung Joon (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Bom, Hee Seung (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Kim, Hyeon Sik (Medical Photonics Research Center, Korea Photonics Technology Institute) ;
  • Kang, Sae Ryung (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Yang, Hyung Jeong (Dept. of Artificial Intelligence Convergence, Chonnam National University)
  • 투고 : 2022.08.12
  • 심사 : 2022.08.23
  • 발행 : 2022.08.31
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초록

Whole body bone scan is the most frequently performed nuclear medicine imaging to evaluate bone metastasis in cancer patients. We evaluated the performance of a VGG16-based transfer learning classifier for bone scan images in which metastatic bone lesion was present. A total of 1,000 bone scans in 1,000 cancer patients (500 patients with bone metastasis, 500 patients without bone metastasis) were evaluated. Bone scans were labeled with abnormal/normal for bone metastasis using medical reports and image review. Subsequently, gradient-weighted class activation maps (Grad-CAMs) were generated for explainable AI. The proposed model showed AUROC 0.96 and F1-Score 0.90, indicating that it outperforms to VGG16, ResNet50, Xception, DenseNet121 and InceptionV3. Grad-CAM visualized that the proposed model focuses on hot uptakes, which are indicating active bone lesions, for classification of whole body bone scan images with bone metastases.

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과제정보

This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) & funded by the Korean government(MSIT) (NRF-2019M3E5D1A02067961) and also supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2B5B01002085).

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