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Life sciences · Preprint
arXiv · August 17, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unrefereed computational study proposing Loki-OT, a machine-learning method that uses optimal transport to improve cell-type classification in histopathology by leveraging tissue context. The method was evaluated on a single independent cohort (TCGA-BRCA) and reported lower patient-level mean absolute error and improved F1 score in epithelium-rich regions compared to a supervised baseline, but lacks peer review, clinical validation, and prospective testing.
Computational algorithm development with validation on an independent retrospective cohort. Cells in breast cancer histopathology images from the independent TCGA-BRCA cohort, specifically those subject to lymphocyte mimicry in epithelium-rich regions.. Intervention: Loki-OT: unbalanced optimal transport method propagating MLLM-derived region-level tissue reasoning to cell-level predictions with weak supervision.. Compared with: Fully supervised in-domain PanopTILs classifier.
Loki-OT achieved lower patient-level MAE than fully supervised PanopTILs classifier on TCGA-BRCA cohort Improved F1 in epithelium-rich mimicry tissues using 278 weak region-level MLLM estimates Method propagates region-level tissue reasoning to cell-level predictions via unbalanced optimal transport
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If peer reviewed and validated prospectively, this method could improve lymphocyte classification in histopathology and reduce pathologist workload in ambiguous cases. However, clinical utility remains unestablished; the work demonstrates technical improvement in a computational task, not clinical outcome benefit.
A methodological proof-of-concept for computational cell classification in histopathology using optimal transport and weak supervision, demonstrating improved performance on a single independent cohort but lacking clinical validation, peer review, and prospective validation.
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If peer reviewed and validated prospectively, this method could improve lymphocyte classification in histopathology and reduce pathologist workload in ambiguous cases. However, clinical utility remains unestablished; the work demonstrates technical improvement in a computational task, not clinical outcome benefit.
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Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context, whereas current vision models either lack contextual reasoning (cell foundation models) or cannot operate at the cell level (pathology MLLMs). We present Loki-OT, which propagates region-level tissue reasoning to individual cell predictions via Unbalanced Optimal Transport, using MLLM-derived density priors as soft guidance for ambiguous cell reassignment. Loki-OT is motivated by the observation that pretrained cell foundation model features already encode discriminative information, including tissue context, but standard cell-level supervision fails to use tissue context effectively. The resulting transport plan is distilled into a lightweight student MLP classifier that learns context-aware decision boundaries within the pretrained feature space. On the independent TCGA-BRCA cohort, Loki-OT achieved lower patient-level MAE than the fully supervised in-domain PanopTILs classifier and improved F1 in epithelium-rich mimicry tissues, using 278 weak region-level MLLM estimates built on a general-domain cell foundation model. Code: https://github.com/xiangli980/Lymphocyte_Mimicry_Correction_via_Loki_OT
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