tatsuyaryu/OsteosarcomaHistopathologyClassification
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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.
| 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| fedavg-v1 | Historical FL round (superseded) | siglip-lora-osteosarcoma-fedavg-v1 |
| 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) |
| Metric | base_v2 | base_v3 |
|---|---|---|
| Tumor sensitivity | 0.955 | 0.985 |
| Critical tumor→NT misses | 5 | 1 |
The montage below embeds histopathology patches for failure-mode review. Research use only — not for clinical decision-making.
Notebooks: notebooks/base_v3_qualitative_research.ipynb, notebooks/base_client_pkg_qualitative_research.ipynb.
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 |
| Label | ID |
|---|---|
| Non-Tumor | 0 |
| Non-Viable-Tumor | 1 |
| Viable | 2 |
# 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
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
| 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 |
| ID | Reference |
|---|---|
| Roots (clean) | Deduplicated tatsuyaryu/OsteosarcomaHistopathologyClassification as client_00_clean |
| PKG (clean) | TCIA Osteosarcoma Tumor Assessment as client_pkg (contamination-filtered) |
big_vision.@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/}
}