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Strategy to implement a convolutional neural network based ideal model observer via transfer learning for multi-slice

Gihun Kim1, Minah Han2,3, Jongduk Baek2,3

  • 1School of Integrated Technology, Yonsei University, Republic of Korea.

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|May 3, 2023
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Transfer learning significantly reduces training samples for convolutional neural network (CNN)-based multi-slice ideal model observers. This approach maintains performance while decreasing sample needs, improving detectability in medical imaging tasks.

Keywords:
CNNbreast CTdeep-learningmodel observertransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Computational Imaging

Background:

  • Deep learning model observers, particularly convolutional neural networks (CNNs), require extensive training data.
  • The number of training samples needed increases with image dimensionality, such as in multi-slice medical imaging.
  • Conventional linear model observers often have limitations in complex detection tasks.

Purpose of the Study:

  • To develop a CNN-based multi-slice ideal model observer utilizing transfer learning (TL-CNN).
  • To reduce the number of training samples required for training deep learning model observers.
  • To evaluate the performance and robustness of the TL-CNN compared to conventional observers.

Main Methods:

  • Generated simulated breast CT image volumes reconstructed using the Feldkamp-Davis-Kress algorithm.
  • Trained and evaluated model observers on background-known-statistically (BKS)/signal-known-exactly and BKS/signal-known-statistically tasks.
  • Compared TL-CNN performance against multi-slice channelized Hotelling observer (CHO) and volumetric CHO, analyzing detectability with varying training samples.

Main Results:

  • The TL-CNN achieved comparable performance with a 91.7% reduction in training samples compared to non-transfer learning approaches.
  • CNN-based observers demonstrated 45% higher detectability in signal-known-statistically tasks and 13% higher in SKE tasks versus linear observers.
  • Filter weight correlation analysis confirmed the effectiveness of transfer learning in multi-slice model observer training.

Conclusions:

  • Transfer learning significantly reduces the data requirements for training deep learning-based multi-slice model observers.
  • The proposed TL-CNN method enhances detection performance in medical imaging tasks without compromising accuracy.
  • This approach offers a practical solution for developing robust AI-driven observers in resource-limited scenarios.