Abstract
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.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/COMPAYL_064.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: https://papers.miccai.org/miccai-2026-sat/supp/COMPAYL_064_supp.pdf
Link to Open Review
BibTex
@InProceedings{LiXia_Lymphocyte_MICCAISAT2026,
author = { Li, Xiang AND Wang, Yuqi AND Heirman, Casey C. AND Heo, Jihye AND Lafata, Kyle J.},
title = { { Lymphocyte Mimicry Correction via Region-Level Tissue Reasoning and Unbalanced Optimal Transport } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17251},
month = {pending},
page = {pending}
}