Abstract
Breast cancer is a significant health challenge, with accurate and timely diagnosis being critical to effective treatment. Immunohistochemistry (IHC) staining is a widely used technique for the evaluation of breast cancer markers, but manual scoring is time-consuming and can be subject to variability. With the rise of Artificial Intelligence (AI), there is an increasing interest in using machine learning and deep learning approaches to automate the scoring of ER, PR and HER2 biomarker in IHC-stained images for effective treatment. In this narrative literature review, we focus on AI-based techniques for the automated scoring of breast cancer markers in IHC-stained images, specifically Allred, Histochemical (H-Score), and HER2 scoring. We aim to identify the current state-of-the-art approaches, challenges, and potential future research prospect for this area of study. By conducting a comprehensive review of the existing literature, we aim to contribute to the ultimate goal of improving the accuracy and efficiency of breast cancer diagnosis and treatment.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callSimilar Papers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
