HER2 Grading of Breast Cancer Patients
Our lab handles large-scale data to extract meaningful insights through statistical modeling, predictive analysis, and data visualization. We aim to support data-driven decision making.
This research focuses on developing AI-driven methods for automated HER2 grading in breast cancer patients using digital pathology images. HER2 status is a critical biomarker for treatment planning and prognosis, but manual assessment can be time-consuming and subjective. By leveraging deep learning and computer vision techniques, this work aims to provide a fast, consistent, and accurate grading system, supporting pathologists in making informed decisions and enhancing personalized cancer care.
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