SigLIP-LoRA Osteosarcoma — base_v3 (federated merge)

Weighted FedAvg of trainable params (LoRA adapters + head) from base_v2 (roots, n=1707) and pkg_hpo_best (PKG, n=544).

Current federated checkpoint for the clean-contract research path. Supersedes historical fedavg-v1 for this story.

Model family

Checkpoint Role Hugging Face
base_v2 Roots single-site HPO best siglip-lora-osteosarcoma-base-v2
pkg_hpo_best PKG private Optuna update siglip-lora-osteosarcoma-pkg-hpo-best
base_v3 (this repo) Federated merge (roots + PKG) siglip-lora-osteosarcoma-base-v3
fedavg-v1 Historical FL round (superseded) siglip-lora-osteosarcoma-fedavg-v1

Holdout results (roots clean test, 410 patches)

Metric base_v2 base_v3 Delta
Overall accuracy 91.22% 90.24% −0.98 pp
Tumor sensitivity 0.973 0.973 0
Tumor → Non-Tumor misses 6 6 0 (preserved)
Non-Tumor → Tumor false alarms 26 31 +5 (more conservative)

PKG external proxy (137 decontaminated test patches)

Metric base_v2 base_v3
Tumor sensitivity 0.955 0.985
Critical tumor→NT misses 5 1

Confusion matrix — base_v2 Confusion matrix — base_v3

Tumor sensitivity and safety bars — base_v2 vs base_v3

UMAP roots vs PKG under base_v3

Cross-dataset near-duplicate hygiene

Error gallery (research use / clinically sensitive)

The montage below embeds histopathology patches for failure-mode review. Research use only — not for clinical decision-making.

Severity-ranked error gallery — base_v3

Notebooks: notebooks/base_v3_qualitative_research.ipynb, notebooks/base_client_pkg_qualitative_research.ipynb.

Model description

Federated Orbax bundle of trainable params only (LoRA + head) on frozen google/siglip-so400m-patch14-384. Not a transformers checkpoint.

Property Value
Framework JAX / Flax
Base model google/siglip-so400m-patch14-384
Merge fedavg_weighted_by_n
Contributors clinic_00 (0.758), clinic_pkg (0.242)
n_total 2251
Image size / norm 384 / SigLIP
Roots holdout accuracy 90.24%
Roots tumor sensitivity 0.973

Classes

Label ID
Non-Tumor 0
Non-Viable-Tumor 1
Viable 2

Reproduce the round

# PKG private HPO from roots, then weighted FedAvg
make pkg-round-from-roots ROUND_OUT=checkpoints/base_v3
# or stepwise:
make pkg-hpo-best
make merge CHECKPOINT_A=checkpoints/base_v2 CHECKPOINT_B=checkpoints/pkg_hpo_best OUT=checkpoints/base_v3
make eval-roots EVAL_CHECKPOINT=checkpoints/base_v3

Deploy / usage

git clone https://github.com/lfniederauer/FederatedLoRA-OsteosarcomaClassification
cd FederatedLoRA-OsteosarcomaClassification
pip install -r requirements.txt
export BIG_VISION_ROOT=~/git/big_vision

python scripts/infer.py --checkpoint checkpoints/base_v3 --image patch.jpg
python scripts/infer.py --hf-repo lfniederauer/siglip-lora-osteosarcoma-base-v3 --image patch.jpg

Publish:

make sync-hf-assets
make publish-hf-base-v3

Files in this repo

File Description
orbax_checkpoint/ Merged trainable weights (LoRA + head)
config.json Model + federated provenance
preprocessor_config.json Size + norm
training_metadata.json / round_metadata.json Merge weights + contributors
assets/ Confusion, safety, UMAP, error gallery, hygiene

Datasets

ID Reference
Roots (clean) Deduplicated tatsuyaryu/OsteosarcomaHistopathologyClassification as client_00_clean
PKG (clean) TCIA Osteosarcoma Tumor Assessment as client_pkg (contamination-filtered)

Limitations

  • Research use only. Error gallery contains clinically sensitive histopathology pixels.
  • Slight roots accuracy drop vs base_v2 is driven by more Non-Tumor→Tumor false alarms, not by increased tumor misses.
  • Requires JAX + big_vision.

Citation

@misc{siglip_lora_osteosarcoma_base_v3,
  title={SigLIP-LoRA base_v3 Federated Osteosarcoma Histopathology Classifier},
  author={lfniederauer},
  year={2026},
  howpublished={\url{https://huggingface.co/lfniederauer/siglip-lora-osteosarcoma-base-v3}}
}

@dataset{leavey2019osteosarcoma_tcia,
  author    = {Leavey, Patrick and Sengupta, Aniruddha and Rakheja, Dinesh and Daescu, Ovidiu and Arunachalam, Harish Babu and Mishra, Rashika},
  title     = {Osteosarcoma data from {UT Southwestern}/{UT Dallas} for Viable and Necrotic Tumor Assessment ({Osteosarcoma-Tumor-Assessment})},
  year      = {2019},
  publisher = {The Cancer Imaging Archive},
  doi       = {10.7937/tcia.2019.bvhjhdas},
  url       = {https://www.cancerimagingarchive.net/collection/osteosarcoma-tumor-assessment/}
}
Downloads last month
18
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Dataset used to train lfniederauer/siglip-lora-osteosarcoma-base-v3