MeowCat-Models

Trained model weights for MeowCat (Multi-resolution Omics-informed Whole-slide Cell Annotation Tool), a deep learning framework that predicts cell-type distributions on H&E whole-slide images using spatially-registered transcriptomics / multiplexed protein imaging as supervision.

Source code: https://github.com/liranmao/MeowCat

MeowCat also ships AI-assistant utilities — Claude Code skills (/meowcat-setup, /meowcat-check) and reusable prompts for Codex / Cursor — that automate configuration, input validation, and pipeline execution. See AI tools.

Repo layout

MeowCat-Models/
├── README.md
├── luad_general_celltype/states/   # LUAD coarse 8-class
├── luad_refined_celltype/states/   # LUAD fine 17-class
├── gbm_codex_celltype/states/      # GBM 14-class (CODEX-supervised)
├── kidney_celltype/states/         # Kidney
└── athero_celltype/states/         # Atherosclerosis 8-class

Each states/ folder contains the ensemble replicas (00/, 01/, ...). Each replica contains model.ckpt and the per-phase subfolders (recon_phase, visium_phase, xenium_phase) produced by MeowCat's multi-phase training.

Models

luad_general_celltype — LUAD coarse 8-class

B, Plasma, Myeloid, Stromal, NonTumor_Epi, Tumor_Epi, T, NK

Recommended when downstream analyses only need broad lineage categories.

luad_refined_celltype — LUAD fine 17-class

B, CD_cDC, Endo, Fibroblast, iCAF, Macro_alveolar, Macro_TAM, Mast,
Mural, myCAF, Neutrophil, NK, NonTumor_Epi, Plasma, T_CD4, T_CD4_Treg,
T_CD8, Tumor_Epi

Recommended for TME-resolved analyses (CAF subtypes, T-cell subsets, macrophage polarization, etc.).

gbm_codex_celltype — GBM CODEX 14-class

AC, MES, MES-Hyp, NPC, OPC, Chromatin-Reg,
Mac, Inflammatory-Mac, T-cell, B-cell,
Neuron, Oligo, Reactive-Ast, Vasc

kidney_celltype — Kidney

Trained with the same MeowCat pipeline on kidney H&E + spatial data. See the MeowCat repo for the cell-type vocabulary and dataset details.

athero_celltype — Atherosclerosis 8-class

Endothelial, Inflammatory, Macrophage, Mast, Neutrophil, Plasma, T, VSMC

Trained on human atherosclerotic plaque (paired healthy / diseased regions), Xenium-only supervision.

Training data

• LUAD — 21 Visium sections (238,488 spots, RCTD soft labels) + 4 Xenium sections (2,047,381 cells, hard labels). Held out: S1, P24. Source: Cancer Cell 2025.
• GBM (CODEX) — 12 IDH-wildtype GBM sections, CODEX → H&E via Warpy. CLS-only (no transcriptomics). Held out: ZH1007_INF, C_1. Source: Cell 2024.
• Kidney — see the MeowCat repo for details. Source: Nature 2026.
• Atherosclerosis — 8 Xenium sections (21,237 cells, hard labels) across 4 patients with paired healthy/diseased plaque regions. 8 additional Xenium sections held out for prediction-only evaluation. Unpublished.

Architecture

Please see the MeowCat repo for details.

Usage

Download

from huggingface_hub import snapshot_download

local = snapshot_download(
    repo_id="liranmao/MeowCat-Models",
    repo_type="model",
    token=True,  # required while the repo is private
)
# weights, e.g.:
#   f"{local}/luad_general_celltype/states/00/model.ckpt"
#   f"{local}/gbm_codex_celltype/states/00/model.ckpt"

To grab only one sub-model:

local = snapshot_download(
    repo_id="liranmao/MeowCat-Models",
    repo_type="model",
    allow_patterns=["gbm_codex_celltype/*", "README.md"],
    token=True,
)

Predict on a new H&E slide

Place the downloaded states/ folder under your MeowCat run's output/batches/states, then run:

meowcat predict --config config.yaml
meowcat visualize --config config.yaml

See examples/06_predict_new_sample/ in the MeowCat repo for an end-to-end prediction-only workflow.

License

CC BY-NC 4.0 — research / non-commercial use only. Source-data restrictions from the underlying studies may apply; please consult the original publications before redistributing predictions.

Citation

If you use these weights, please cite both MeowCat and the source datasets:

@software{meowcat,
  title  = {MeowCat: Multi-resolution Omics-informed Whole-slide Cell Annotation Tool},
  author = {Mao, Liran and contributors},
  year   = {2026},
  url    = {https://github.com/liranmao/MeowCat}
}

• LUAD: Cancer Cell 2025 — S1535-6108(25)00445-3
• GBM: Cell 2024 — S0092-8674(24)00320-9
• Kidney: Nature 2026 — s41586-026-10363-4
• Atherosclerosis: unpublished