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README.md

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+---
+license: apache-2.0
+task_categories:
+  - image-classification
+  - feature-extraction
+tags:
+  - medical
+  - pathology
+  - radiology
+  - clinical
+  - oncology
+  - whole-slide-image
+  - dicom
+  - multimodal
+size_categories:
+  - n<1K
+pretty_name: HoneyBee Sample Files
+---
+
+# HoneyBee Sample Files
+
+Sample data for the [HoneyBee](https://github.com/Lab-Rasool/HoneyBee) framework — a scalable, modular toolkit for multimodal AI in oncology.
+
+These files are used by the HoneyBee example notebooks to demonstrate clinical, pathology, and radiology processing pipelines.
+
+**Paper**: [HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models](https://arxiv.org/abs/2405.07460)
+**Package**: [`pip install honeybee-ml`](https://pypi.org/project/honeybee-ml/)
+
+## Files
+
+| File | Type | Size | Description |
+|------|------|------|-------------|
+| `sample.PDF` | Clinical | 70 KB | De-identified clinical report (PDF) for NLP extraction |
+| `sample.svs` | Pathology | 146 MB | Whole-slide image (Aperio SVS) for tissue detection, patch extraction, and embedding |
+| `CT/` | Radiology | 105 MB | CT scan with 2 DICOM series (205 slices total) for radiology preprocessing |
+
+### CT Directory Structure
+
+```
+CT/
+├── 1.3.6.1.4.1.14519.5.2.1.6450.4007.1209.../ (101 slices)
+└── 1.3.6.1.4.1.14519.5.2.1.6450.4007.2906.../ (104 slices)
+```
+
+## Usage
+
+Install HoneyBee:
+
+```bash
+pip install honeybee-ml[all]
+```
+
+### Pathology — Load a whole-slide image
+
+```python
+from huggingface_hub import hf_hub_download
+from honeybee.loaders.Slide.slide import Slide
+from honeybee.processors.wsi import PatchExtractor
+
+slide_path = hf_hub_download(
+    repo_id="Lab-Rasool/honeybee-samples",
+    filename="sample.svs",
+    repo_type="dataset",
+)
+
+slide = Slide(slide_path)
+slide.detect_tissue(method="otsu")
+patches = PatchExtractor(patch_size=256).extract(slide)
+print(f"Extracted {len(patches)} patches from {slide.dimensions}")
+```
+
+### Clinical — Process a clinical PDF
+
+```python
+from huggingface_hub import hf_hub_download
+from honeybee.processors import ClinicalProcessor
+
+pdf_path = hf_hub_download(
+    repo_id="Lab-Rasool/honeybee-samples",
+    filename="sample.PDF",
+    repo_type="dataset",
+)
+
+processor = ClinicalProcessor()
+result = processor.process(pdf_path)
+print(result["entities"])
+```
+
+### Radiology — Download CT DICOM series
+
+```python
+from huggingface_hub import snapshot_download
+
+ct_dir = snapshot_download(
+    repo_id="Lab-Rasool/honeybee-samples",
+    repo_type="dataset",
+    allow_patterns="CT/**",
+)
+```
+
+## Citation
+
+```bibtex
+@article{rasool2024honeybee,
+  title={HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models},
+  author={Rasool, Ghulam and others},
+  journal={arXiv preprint arXiv:2405.07460},
+  year={2024}
+}
+```
+
+## License
+
+Apache 2.0 — see the [HoneyBee repository](https://github.com/Lab-Rasool/HoneyBee) for details.