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Convolutional neural networks for whole slide image superresolution.

Lopamudra Mukherjee1, Adib Keikhosravi2, Dat Bui1

  • 1Department of Computer Science, University of Wisconsin Whitewater, Whitewater, WI 53190, USA.

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We developed a computational method using convolutional neural networks (CNNs) to enhance low-resolution pathology images. This approach improves image resolution for diagnostic use, making high-quality scans more accessible and affordable.

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Area of Science:

  • Digital Pathology
  • Computational Imaging
  • Biomedical Engineering

Background:

  • Low-magnification slide scanners offer cost-effective and efficient image acquisition but yield lower resolution images.
  • Current low-resolution images lack the diagnostic quality required for clinical and research settings.
  • Existing image super-resolution methods are not optimized for the unique challenges of slide scanner data.

Purpose of the Study:

  • To investigate the feasibility of enhancing low-resolution slide scanner images to diagnostic quality.
  • To develop a computational approach for image super-resolution specifically for pathology slide images.
  • To enable the use of affordable low-resolution scanners in clinical and research applications.

Main Methods:

  • A convolutional neural network (CNN) model was specifically designed and trained for image super-resolution.
  • The CNN was trained on low-resolution cancer slide images.
  • Computational analysis was used to validate the quantitative improvements in image resolution.

Main Results:

  • The proposed CNN-based method successfully enhanced the resolution of low-quality slide scanner images.
  • Enhanced images demonstrated comparable quality and quantitative measures to those from high-resolution scanners.
  • The super-resolution technique achieved results similar to high-resolution scans.

Conclusions:

  • The developed computational approach effectively improves the resolution of low-magnification slide scanner images.
  • This method bridges the gap between low-cost scanning technology and diagnostic image requirements.
  • The approach expands the utility of low-resolution scanners, offering benefits in cost, access, and speed for research and clinical use.