mlsanityData to 13 Oct 2026

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

arXiv 2410.17557 ↗First posted 23 Oct 2024eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 15 of its 54 references that are in the map. First posted on arXiv 23 Oct 2024.

Builds on

15 of 54 references.

References · 15

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3 more
  1. 2024Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Datacited by 3
  2. 2024Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Samplingcited by 3
  3. 2021Increasing a microscope's effective field of view via overlapped imaging and machine learningcited by 2

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learningcited by 0