taejoon89/openpath

OpenPath: Public-Data Pathology Foundation Models and Leakage-Free Evaluation

Training, reproduction, and evaluation code.

🔗 [GitHub](https://github.com/taejoon89/openpath) · Checkpoints · Corpus

OpenPath is a vision foundation model for computational pathology: a ViT-g/14 encoder pre-trained with self-supervision (DINOv2 + gram anchoring) on public-only whole-slide histopathology tiles. This repository contains the training, reproduction, and evaluation code plus the released weight (teacher_checkpoint.pth = training_316250). The corpus and the full checkpoint set are hosted separately (see below).

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 (mean Pearson: OpenPath 0.323 > UNI2-h 0.301 > OpenMidnight 0.300 > Virchow2 0.292 > prov-gigapath 0.286 > Phikon-v2 0.274 > UNI 0.257). Released checkpoint: `training_316250` (in openpath-checkpoints). See Evaluation.

• —Encoder: ViT-g/14 (reg4), 1536-dim CLS embedding
• —Objective: DINO + iBOT + KDE (DINOv2) with gram anchoring (technique from DINOv3, re-implemented)
• —Data: public pathology WSIs only (TCGA, TCIA, GTEx, CAMELYON, ACROBAT, SurGen, …), re-tiled at native 40×
• —Warm start: Meta DINOv2 ViT-g/14-reg
• —Training: FSDP (SHARDGRADOP), bf16, flat learning-rate schedule, 40× B200 (multi-node)

Repository layout

OpenPath/                                      # DINOv2 training fork (derived from OpenMidnight)
  dinov2/train/train.py                        # training loop (+ gram-weight schedule)
  dinov2/train/ssl_meta_arch.py                # SSL arch (+ frozen gram-anchor teacher)
  dinov2/loss/gram_loss.py                     # gram anchoring loss (clean-room re-impl, Apache-2.0)
  dinov2/data/openpath_wds.py                  # WebDataset loader for the OpenPath corpus
  dinov2/configs/train/openpath_vitg14.yaml    # training config
scripts/
  launch.sh                  # multi-node launcher (host + workers, 40 GPU)
  autoresume.sh              # crash-tolerant auto-resume
  watch_eval.sh              # online HEST probing per checkpoint
  run_hest_3way.py           # HEST evaluation (Meta DINOv2 / Phikon-v2 / OpenPath)
eval/                                          # downstream benchmark / reference-FM comparison
  openpath_eva_backbone.py   # backbone factories: OpenPath + Phikon / OpenMidnight / UNI / UNI2-h / gigapath / Virchow2
  st_bench.py                # AMC-HCC-ST benchmark (LOPO ridge, headline)
  run_patch_eval.sh          # PCam / CRC / BACH patch probing via kaiko-eva
  run_hest_ref.py            # HEST-1K for reference FMs (UNI / UNI2-h / gigapath / Virchow2)
  eva_configs/               # eva YAML configs (crc / bach / patch_camelyon)
requirements.txt

Related artifacts

The training config points to the corpus via train.sample_list_path: "openpath:glob=<corpus>/*/tiles/shards/w*/*.tar". The gram anchor (gram.ckpt) is an earlier OpenPath teacher checkpoint from openpath-checkpoints.

Method — gram anchoring

Long self-supervised training degrades dense/patch features. Following DINOv3, we add a gram anchoring loss: the MSE between the L2-normalized patch-token Gram (similarity) matrices of the student and a frozen anchor model (a strong earlier checkpoint). The loss weight is 40 and it activates near the dense-feature peak (iteration 57,500) with a 3k-iter ramp. This dampens the post-peak decline of dense representations while DINO/iBOT keep optimizing the global representation.

Key hyper-parameters

Reproducing training

export PYTHONPATH="$PWD/OpenPath"
CFG=OpenPath/dinov2/configs/train/openpath_vitg14.yaml
# edit CFG: train.sample_list_path (corpus glob), gram.ckpt (anchor checkpoint), MODEL.WEIGHTS (DINOv2 warm-start)
# set your cluster (see scripts/launch.sh header): MASTER_NODE, WORKER_NODES, MASTER_ADDR, NCCL_IB_HCA, *_SOCKET_IFNAME
export MASTER_NODE=node1 WORKER_NODES="node2 node3 node4 node5" MASTER_ADDR=<master-ib-ip>
bash scripts/launch.sh openpathrun "$CFG" <output_dir> <log_dir>
# optionally run scripts/autoresume.sh (background) and scripts/watch_eval.sh (online HEST)

Extract CLS embeddings for downstream use (teacher_checkpoint.pth = the released training_316250, included in this repo):

import torch, dinov2.models.vision_transformer as vits
ck = torch.load("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)

Evaluation

Frozen-encoder linear/ridge probing. The headline benchmark is AMC-HCC-ST — a contamination-free in-house Asan Medical Center hepatocellular-carcinoma Visium spatial-transcriptomics cohort (leave-one-patient-out, mean Pearson over top-50 highly-variable genes) — no public FM was trained on it, so it is the least leakage-prone comparison. The reported OpenPath checkpoint is training_316250.

Comparison — all 7 models loaded through one backbone factory and probed under an identical protocol; sorted by the clean AMC-HCC-ST benchmark:

On the contamination-free AMC-HCC-ST cohort OpenPath ranks #1 among all seven foundation models. The picture inverts on the public benchmarks (HEST-1K, CRC, BACH): there OpenPath is mid-pack to low, and the large FMs lead. Those benchmarks derive from public repositories (TCGA/GTEx/etc.) that these FMs were pre-trained on, so their apparent edge is confounded by train/test leakage — which is exactly why the leakage-free AMC-HCC-ST cohort is our headline. (The reported checkpoint training_316250 is selected by AMC-HCC-ST; OpenPath's HEST-1K peaks earlier in training at ~0.38.) PCam / CAMELYON is excluded because it overlaps our own training corpus.

Reproducing the comparison

All models are loaded through a single backbone-factory module (eval/openpath_eva_backbone.py) and probed under an identical protocol, so OpenPath and the reference FMs (Phikon-v2, OpenMidnight, UNI, UNI2-h, gigapath, Virchow2) are directly comparable.

export PYTHONPATH="$PWD/OpenPath:$PWD/eval"
# Headline: AMC-HCC-ST (LOPO ridge; cohort is private, code is provided)
python eval/st_bench.py --backbone openpath --weights <teacher_checkpoint.pth>
python eval/st_bench.py --backbone uni              # reference FM (also: uni2 / gigapath / virchow2 / phikon / openmidnight)

# Patch probing (PCam / CRC / BACH) via kaiko-eva
bash eval/run_patch_eval.sh openpath crc <teacher_checkpoint.pth>
bash eval/run_patch_eval.sh uni crc                 # reference FM

Reference FM weights are pulled from their Hugging Face hubs on first use (UNI / UNI2-h / Virchow2 are gated — request access on HF beforehand).

Evaluate your model on AMC-HCC-ST — we run it for you

AMC-HCC-ST is an in-house, contamination-free spatial-transcriptomics cohort that we are actively curating and expanding at Asan Medical Center. Because it is patient-derived, the cohort cannot be publicly redistributed. Rather than keep it as an internal-only benchmark, we offer to run the evaluation on your behalf — send us your pathology encoder and we return its AMC-HCC-ST score under the exact protocol used above (leave-one-patient-out ridge, top-50 HVG, mean Pearson), directly comparable to the reference models.

What to send

• —Weights — a teacher_checkpoint.pth / state_dict, or a public Hugging Face / timm hub id.
• —A loader — a small build() returning an nn.Module that maps a normalized (B, 3, 224, 224) batch to a (B, d) tile embedding (CLS or pooled), plus the expected input normalization (ImageNet by default). See eval/openpath_eva_backbone.py for the exact interface we use.
• —Optional — a one-line model description and license so we can report your result correctly.

Every submission runs through the same single backbone-factory + probing pipeline (eval/), so your numbers are apples-to-apples with the table above. This keeps the benchmark leakage-controlled and open to the community even though the underlying data stays private.

Contact: open a discussion on the `taejoon89/openpath` model repo, or email taejoon@amc.seoul.kr.

Intended use & limitations

Intended use. OpenPath is a frozen feature extractor for H&E histopathology. It produces a 1536-dim CLS embedding per 224×224 tile (native ~40× / 0.5 µm-per-pixel regime, ImageNet normalization) for downstream linear/ridge probing, k-NN, MIL aggregation, and retrieval. It is a research artifact, not a medical device, and must not be used for diagnosis or clinical decision-making.

Limitations.

• —Public-benchmark leakage. Public benchmarks (HEST-1K, NCT-CRC-HE, BACH) derive from repositories (TCGA/GTEx/…) that many foundation models — and partly OpenPath — were pre-trained on. Absolute numbers and cross-model rankings on them are confounded; prefer leakage-controlled evaluation.
• —Checkpoint trade-off. The released training_316250 is selected by the clean AMC-HCC-ST benchmark; earlier checkpoints score higher on HEST-1K (~0.38). Pick a checkpoint to match your downstream task.
• —Domain. Trained on H&E WSIs at native magnification. Behavior on IHC, cytology, frozen sections, non-0.5 µm-per-pixel inputs, or non-pathology images is untested.
• —Patch-level encoder. OpenPath encodes tiles independently; slide-level context requires a separate aggregator (future work).

Citation

A paper is in preparation. Until then, please cite the repository and the upstream work it builds on:

@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}
}

OpenPath builds on DINOv2, OpenMidnight / Midnight, and gram anchoring (DINOv3) — see OpenPath/README.md for the full upstream citations, which should also be cited.

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

Code — Apache-2.0. This repository is a fork of DINOv2 / OpenMidnight (both Apache-2.0); see OpenPath/LICENSE. The gram-anchoring loss (OpenPath/dinov2/loss/gram_loss.py) is a clean-room re-implementation of the DINOv3 technique — written from its mathematical description and verified to be numerically equivalent — so it is Apache-2.0 as well, and the codebase contains no non-commercial (DINOv3-licensed) code.

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 (redistributable).