Kirti Prakash

Abstract Accurate phenotyping of cells in the tumor microenvironment is essential for understanding cancer biology but typically requires precise cell segmentation, limiting scalability. Here, we introduce Contrastive Learning Enabled Accurate Registration of Immune and Tumor cells (CLEAR-IT), a self-supervised framework that learns cell-level features from multiplexed images using only cell locations.

Corresponding Author Kirti Prakash Faculty of Mechanical, Maritime, and Materials Engineering, Delft Center for Systems and Control, Technische Universiteit Delft, Delft, The Netherlands Correspondence Kirti Prakash, Faculty of Mechanical, Maritime, and Materials Engineering, Delft Center for Systems and Control, Technische Universiteit Delft, Delft, The Netherlands. Email: kirtiprakash2.71@gmail.com Search for more papers by this author

Abstract Accurate phenotyping of cells in the tumor microenvironment is crucial for understanding cancer biology and developing effective therapies. However, current methods require precise cell segmentations and struggle to generalize across different imaging modalities, limiting their utility in digital pathology.