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

arXiv: https://arxiv.org/abs/2410.17557 · HTML: https://mlsanity.com/paper/2410.17557/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning](https://mlsanity.com/paper/1807.07701/) (2018)
- [Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization](https://mlsanity.com/paper/2204.10533/) (2022)
- [Phase Imaging with Computational Specificity (PICS) for measuring dry mass changes in sub-cellular compartments](https://mlsanity.com/paper/2002.08361/) (2020)
- [Super-resolution image display using diffractive decoders](https://mlsanity.com/paper/2206.07281/) (2022)
- [Unidirectional Imaging using Deep Learning-Designed Materials](https://mlsanity.com/paper/2212.02025/) (2022)
- [Deep learning enhanced mobile-phone microscopy](https://mlsanity.com/paper/1712.04139/) (2017)
- [Illumination Pattern Design with Deep Learning for Single-Shot Fourier Ptychographic Microscopy](https://mlsanity.com/paper/1810.03481/) (2018)
- [eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging](https://mlsanity.com/paper/2301.03162/) (2023)
- [All-Optical Phase Conjugation Using Diffractive Wavefront Processing](https://mlsanity.com/paper/2311.04473/) (2023)
- [Information hiding cameras: optical concealment of object information into ordinary images](https://mlsanity.com/paper/2401.07856/) (2024)
- [Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers](https://mlsanity.com/paper/2205.00428/) (2022)
- [Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data](https://mlsanity.com/paper/2403.14324/) (2024)
- [Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling](https://mlsanity.com/paper/2404.00837/) (2024)
- [Increasing a microscope's effective field of view via overlapped imaging and machine learning](https://mlsanity.com/paper/2110.04921/) (2021)

## Built on by (most cited first, 1 of 1)

- [Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning](https://mlsanity.com/paper/2601.18219/) (2026)

## 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.