Modelpublic

Noel-Niko/dinov2-upernet-20260322-histology-annotation-human

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

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Model Card

Brain Region Segmentation — Human Brain (Allen Depth-3)

DINOv2-Large + UperNet model fine-tuned for semantic segmentation of human brain regions in Nissl-stained histological sections.

Model Details

Attribute	Value
Architecture	DINOv2-Large (304M) + UperNet (38M)
Classes	44 (depth-3 brain regions)
Input Size	518x518
Training Data	Allen Human Brain Atlas (6 donors, Nissl staining)
mIoU (val center-crop)	65.5%
mIoU (test sliding window)	65.0%
Pixel accuracy (test)	99.1%

Usage

bash

git clone https://github.com/Noel-Niko/histological-image-analysis
cd histological-image-analysis
make install
make download-models-human-allen
make annotate-human-allen IMAGES=/path/to/your/slides/

Paper

Cross-Species Transfer of Ultra-Fine-Grained Brain Segmentation: From Mouse to Human with DINOv2 + UperNet

We extend the DINOv2-Large + UperNet approach from mouse (1,328 classes, 79.1% mIoU) to human brain tissue using the Allen Human Brain Atlas (sparse SVG annotations, 6 donors). The depth-3 model (44 brain regions) achieves 65.5% val CC mIoU and 65.0% test SW mIoU with 99.1% pixel accuracy. Major structures (cerebellum, cerebral cortex, thalamus, pons) exceed 99% IoU.

See `paper.md` in this repo for the full paper.

Citation

If you use this model, please cite the training data sources and the paper included in this repository.

Repository

Full source code, training notebooks, and all models: https://github.com/Noel-Niko/histological-image-analysis

Maintaining This Repo

To update model weights, papers, or this README:

bash

cd histological-image-analysis
export HUGGING_FACE_TOKEN=hf_your_token_here

# Update model weights (Databricks or local):
jupyter notebook notebooks/upload_models_to_hf.ipynb

# Update papers + READMEs (local only):
jupyter notebook notebooks/upload_papers_to_hf.ipynb