BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866918115531554816 |
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| author | Fanous, Michael John Seybold, Christopher Michael Chen, Hanlong Pillar, Nir Ozcan, Aydogan |
| author_facet | Fanous, Michael John Seybold, Christopher Michael Chen, Hanlong Pillar, Nir Ozcan, Aydogan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_17557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images Fanous, Michael John Seybold, Christopher Michael Chen, Hanlong Pillar, Nir Ozcan, Aydogan Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics 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. |
| title | BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2410.17557 |