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arXiv Machine Learning By Xiang Li, Yuqi Wang, Casey C. Heirman, Jihye Heo, Kyle J. Lafata

Lymphocyte Mimicry Correction via Region-Level Tissue Reasoning and Unbalanced Optimal Transport

Read the original on arXiv Machine Learning →

Loki-OT corrects lymphocyte mimicry by transferring region‑level tissue reasoning to individual cell predictions through Unbalanced Optimal Transport. It uses density priors from a pathology MLLM to guide ambiguous cell reassignment and distills the resulting transport plan into a lightweight MLP that learns context‑aware decision boundaries within pretrained cell‑foundation features. On the TCGA‑BRCA cohort, Loki‑OT outperformed a fully supervised PanopTILs classifier in patient‑level MAE and improved F1 scores in epithelium‑rich mimicry tissues.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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