Nuclei Segmentation

Nuclei segmentation aims to accurately identify individual cell nuclei in histopathology images, a critical step for quantitative pathology and disease diagnosis. However, challenges such as overlapping, clustered, or ambiguous nuclei make this task highly complex.

Our group focuses on advancing this field through two key research directions:

• Weakly Supervised & Noise-Robust Learning – reducing reliance on costly pixel-wise annotations.

• Domain Generalisation – developing models that remain robust across unseen staining styles, scanners, and clinical settings.