<ul><li>Researchers have developed a spatially-lucid classifier for analyzing immune-tumor relationships and designing new immunotherapies in oncology.</li><li>The classifier can distinguish between two classes based on the arrangements of their multi-category point sets.</li><li>Unlike previous techniques, the proposed framework can handle significant spatial variability within a single place-type.</li><li>Experimental results on real-world MxIF oncology data demonstrate higher prediction accuracy compared to baseline methods.</li></ul>

Towards Spatially-Lucid AI Classification in Non-Euclidean Space: An Application for MxIF Oncology Data

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