Nuclei Segmentation in Histopathology Images

This research project focuses on developing advanced AI algorithms for accurate nuclei segmentation in histopathology images, a critical step in computational pathology. Precise segmentation of cell nuclei is essential for downstream tasks such as tumor grading, biomarker quantification, and patient prognosis. The project leverages deep learning models, including convolutional neural networks (CNNs) and transformer-based architectures, to detect and delineate nuclei across diverse staining conditions and tissue types. By addressing challenges such as overlapping nuclei, staining variability, and complex tissue morphology, this work aims to improve the reliability and scalability of digital pathology workflows and support more precise and automated cancer diagnosis.