BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images.

Journal: NPJ digital medicine
Published Date: Aug 6, 2025

Abstract

We developed a rapid scanning optical microscope, termed "BlurryScope", that leverages continuous image acquisition and deep learning to provide a cost-effective and compact solution for automated inspection and analysis of tissue sections. This device offers comparable speed to commercial digital pathology scanners, but at a significantly lower price point and smaller size/weight. Using BlurryScope, we implemented automated classification of human epidermal growth factor receptor 2 (HER2) scores on motion-blurred images of immunohistochemically (IHC) stained breast tissue sections, achieving concordant results with those obtained from a high-end digital scanning microscope. Using a test set of 284 unique patient cores, we achieved testing accuracies of 79.3% and 89.7% for 4-class (0, 1+, 2+, 3+) and 2-class (0/1+, 2+/3+) HER2 classification, respectively. BlurryScope automates the entire workflow, from image scanning to stitching and cropping, as well as HER2 score classification.

Authors

  • Michael John Fanous
    Electrical and Computer Engineering Department, University of California, Los Angeles, 90095, CA, USA.
  • Christopher Michael Seybold
    Mathematics Department, University of California, Los Angeles, 90095, CA, USA.
  • Hanlong Chen
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Nir Pillar
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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