tanjimin/CANVAS
Official repository for Characterization of tumor heterogeneity through segmentation-free representation learning on multiplexed imaging data
This project helps cancer researchers analyze tumor tissue from multiplexed imaging data, such as IMC or TIFF files, to understand its cellular composition and heterogeneity without needing to manually outline individual cells. It takes your raw imaging files and channel information, processes them, and then outputs a trained model and extracted biological features for further downstream analysis. Scientists and pathologists studying tumor microenvironments will find this useful.
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Use this if you need to characterize tumor heterogeneity and extract meaningful biological features from multiplexed tissue images without performing complex cell segmentation.
Not ideal if you are working with standard brightfield images or need precise, individual cell segmentation for your analysis.
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Language
Python
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Last pushed
Sep 28, 2025
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