Light weight deep learning-based auto-quantification system for bright-filed HER2 dual in situ hybridization image analysis
Huang, C.-Y.; Lin, J.-R.; Huang, P.-C.; Liao, C.-H.; Yen, C.-C.; Chu, L.-A.
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The evaluation of erb-b2 receptor tyrosine kinase 2 (ERBB2 or HER2) gene amplification status through Dual in Situ Hybridization (DISH) currently relies on manual assessment by pathologists. There are several deep learning-based algorithms for H&E or ISH analysis. However, DISH analysis tools are still lacking. We developed a fully automated deep learning-based quantification system to assist pathologists in identifying the most relevant cells throughout the entire DISH image. In the comparison between pathologists and the auto-quantification system, the overall percentage agreement (OPA) by case was 88. 9% (80/90). These results demonstrate that each image, with a processing time of approximately 1 minute, achieves similar results compared to pathologists assessments, while the manual procedure will take 10-20 times longer to examine the same specimen. This approach offers a versatile system for bright-field HER2 DISH image analysis. The system provides faster, cheaper, standardized, and versatile diagnostic tools to aid pathologists in the HER2 DISH diagnostic process.
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