taejoon89/openpath-checkpoints

OpenPath — Checkpoints

Teacher checkpoints of OpenPath, a ViT-g/14 pathology foundation model pre-trained with self-supervision (DINOv2 + gram anchoring) on public-only whole-slide histopathology tiles (OpenPath corpus).

Headline result. On AMC-HCC-ST — a contamination-free in-house Asan Medical Center hepatocellular-carcinoma spatial-transcriptomics cohort, the least leakage-prone benchmark since no public foundation model was trained on it — OpenPath ranks #1 among seven foundation models.

Checkpoints

• 61 teacher checkpoints: training_0 … training_345000, every 5,750 iters (≈ 1 native epoch total).
• Each is training_<iter>/teacher_checkpoint.pth (ViT-g/14 reg4, 1536-dim CLS embedding).
• Released model = training_316250 — selected by the clean AMC-HCC-ST benchmark. (OpenPath's HEST-1K peaks earlier, ~`training_23000` at ~0.38; pick a checkpoint to match your task.)

Load & extract embeddings

Requires the OpenPath / DINOv2 code (taejoon89/openpath).

import torch, dinov2.models.vision_transformer as vits
ck = torch.load("training_316250/teacher_checkpoint.pth", map_location="cpu", weights_only=False)
sd = {k[len("backbone."):]: v for k, v in ck["teacher"].items() if k.startswith("backbone.")}
m = vits.vit_giant2(patch_size=14, img_size=224, block_chunks=4, num_register_tokens=4,
                    ffn_layer="swiglufused", init_values=1e-5,
                    interpolate_antialias=True, interpolate_offset=0.0)
m.load_state_dict(sd, strict=True); m.eval()
cls = m.forward_features(x)["x_norm_clstoken"]   # (B, 1536), ImageNet-normalized 224x224 input

Evaluation

Frozen-encoder linear/ridge probing, all models under one protocol (sorted by the clean AMC-HCC-ST cohort). AMC-HCC-ST is our headline because public benchmarks (HEST-1K, CRC, BACH) derive from repositories these FMs were pre-trained on and are confounded by train/test leakage.

Model	AMC-HCC-ST (clean) ↓	HEST-1K (public)	NCT-CRC-HE (9-cls acc)	BACH (4-cls acc)
OpenPath (training_316250)	0.323	0.372	0.954	0.761
UNI2-h	0.301	0.414	0.966	0.908
OpenMidnight	0.300	0.390	0.967	0.906
Virchow2	0.292	0.398	0.964	0.875
prov-gigapath	0.286	0.393	0.953	0.752
Phikon-v2	0.274	0.375	0.937	0.708
UNI	0.257	0.386	0.946	0.777

Intended use & limitations

Frozen feature extractor for H&E histopathology tiles (native ~40× / 0.5 µm-per-pixel, ImageNet normalization) → 1536-dim CLS embedding for linear/ridge probing, k-NN, MIL, retrieval. Not a medical device; not for diagnosis. Public-benchmark numbers are leakage-confounded; it is a patch-level encoder (slide-level context needs a separate aggregator). See the model / code card for details.

Related artifacts

Artifact	Hugging Face repo	Notes
Corpus	taejoon89/openpath-corpus	Native 40× pathology tiles, 33,991 WebDataset shards / ~17 TB
Checkpoints	taejoon89/openpath-checkpoints	This repository
Code	taejoon89/openpath	training & evaluation code (also on GitHub)

Citation

@misc{openpath2026,
  title  = {OpenPath: Public-Data Pathology Foundation Models and Leakage-Free Evaluation},
  author = {Tae Joon Jun},
  year   = {2026},
  note   = {https://huggingface.co/taejoon89/openpath}
}

Acknowledgements

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR21C0198); the Advanced GPU Utilization Support Program funded by the Government of the Republic of Korea, Ministry of Science and ICT; and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (grant number: RS-2026-25522634).

License

Weights — Apache-2.0 (warm-started from Meta DINOv2 ViT-g/14-reg, itself Apache-2.0). Training data: public pathology datasets under CC-BY / CC0 / NIH-open terms.