---
license: mit
tags:
- ophthalmology
- object-detection
- image-classification
- medical-imaging
- fundus
- oct
- detectron2
- figure-parsing
- pytorch
---

# PubMed-Ophtha Detection Models

This repository contains the three detection and classification models used in the
[PubMed-Ophtha](https://arxiv.org/abs/2605.02720) dataset pipeline for parsing
ophthalmological figures from scientific publications.

**Paper:** Hallitschke V.J., Eickhoff C., Berens P. *PubMed-Ophtha: An open resource
for training ophthalmology vision-language models on scientific literature.* arXiv:2605.02720 (2026).

## Models

The repository contains three model checkpoints under `models/`:

| Directory | Checkpoint | Framework | Architecture | Task | Classes |
|---|---|---|---|---|---|
| `imaging_type_detection_1515892632/` | `model_0003909.pth` | PyTorch (Detectron2) | RetinaNet + ResNet FPN | Image type detection | CFP, OCT, Retinal Imaging, Other |
| `panel_detection_1020880423/` | `model_0026865.pth` | PyTorch (Detectron2) | RetinaNet + ResNet FPN | Panel & identifier detection | Panel, Label |
| `mark_status_classifier_482239176/` | `model_epoch_7.pth` | PyTorch | ResNet-50 | Mark status classification | Plain, Annotated |

Each Detectron2 model directory also contains a `config.yaml` required for inference.

### Panel Detection Model

Detects panels and panel identifier labels (e.g. "A", "B") within multi-panel figures.
Trained on the PubMed-Ophtha-Annotation dataset merged with
[PanelSeg](https://doi.org/10.1145/3331184.3331253) and
[ImageCLEF2016](https://www.imageclef.org/2016/medical), starting from an
ImageCLEF2016-pretrained checkpoint.

- **mAP@0.50:** 0.909 (panels), 0.903 (panel identifiers)
- **mAP@0.95:** 0.532 (panels), 0.018 (panel identifiers)

### Image Type Detection Model

Detects individual images within a panel and assigns each a retinal imaging modality:
color fundus photography (CFP), optical coherence tomography (OCT), retinal imaging
(ultra-wide field / fluorescein angiography), or other (graphs, ultrasound, etc.).

- **mAP@0.50:** 0.892
- **mAP@0.95:** 0.558

### Mark Status Classifier

A ResNet-50 binary classifier applied to cropped image regions detected by the image
type model. Predicts whether an image contains annotation marks such as arrows, dots,
or bounding boxes.

- **Accuracy:** 89.5% on the held-out test set

## Usage

Models are consumed by the [`pubmed-ophtha`](https://github.com/berenslab/pubmed-ophtha)
Python package. Download all weights with:

```bash
pip install pubmed-ophtha
pubmed-ophtha-split pull-models --local-dir .
```

Or download directly via `huggingface_hub`:

```python
from huggingface_hub import snapshot_download

snapshot_download(repo_id="pubmed-ophtha/detection-models", local_dir=".")
```

After downloading, run inference via the `DetectronFigureSplitter`:

```python
from pubmed_ophtha.figure_splitting.detectron_figure_splitter import DetectronFigureSplitter
from pubmed_ophtha.const.models import get_default_model_args

splitter = DetectronFigureSplitter(**get_default_model_args())

with open("figure.png", "rb") as f:
    image_bytes = f.read()

predictions = splitter.predict(image_bytes)
# Keys: pred_boxes, pred_classes, scores,
#       secondary_pred_classes, secondary_scores, keep_after_nms
```

`pred_classes` contains Panel/Label detections from the panel detection model followed
by CFP/OCT/Retinal Imaging/Other detections from the image type model.
`secondary_pred_classes` contains the Plain/Annotated mark status for each image
detection (set to `"None"` for panel detections).

## Training

Both RetinaNet models use a ResNet backbone with FPN, finetuned from an
ImageCLEF2016-pretrained Detectron2 checkpoint on the PubMed-Ophtha-Annotation
dataset. The ResNet-50 classifier was trained from an ImageNet-pretrained checkpoint
for 35 epochs with random cropping, flips, affine transformations, and color
augmentation.

## Dataset

The ground-truth annotations used for training are available as part of the
PubMed-Ophtha dataset:
[huggingface.co/datasets/pubmed-ophtha/PubMed-Ophtha](https://huggingface.co/datasets/pubmed-ophtha/PubMed-Ophtha)

## Citation

```bibtex
@article{hallitschke2026pubmed,
  title={{PubMed-Ophtha}: An open resource for training ophthalmology vision-language models on scientific literature},
  author={Hallitschke, Verena Jasmin and Eickhoff, Carsten and Berens, Philipp},
  journal={arXiv preprint arXiv:2605.02720},
  year={2026}
}
```

## License

MIT — see [LICENSE](LICENSE).