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  ---
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- license: apache-2.0
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  task_categories:
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  - image-classification
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  - feature-extraction
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  └── 1.3.6.1.4.1.14519.5.2.1.6450.4007.2906.../ (104 slices)
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  ```
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- ## Usage
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-
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- Install HoneyBee:
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-
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- ```bash
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- pip install honeybee-ml[all]
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- ```
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-
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- ### Pathology — Load a whole-slide image
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-
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- ```python
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- from huggingface_hub import hf_hub_download
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- from honeybee.loaders.Slide.slide import Slide
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- from honeybee.processors.wsi import PatchExtractor
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-
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- slide_path = hf_hub_download(
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- repo_id="Lab-Rasool/honeybee-samples",
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- filename="sample.svs",
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- repo_type="dataset",
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- )
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-
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- slide = Slide(slide_path)
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- slide.detect_tissue(method="otsu")
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- patches = PatchExtractor(patch_size=256).extract(slide)
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- print(f"Extracted {len(patches)} patches from {slide.dimensions}")
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- ```
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-
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- ### Clinical — Process a clinical PDF
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-
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- ```python
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- from huggingface_hub import hf_hub_download
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- from honeybee.processors import ClinicalProcessor
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-
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- pdf_path = hf_hub_download(
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- repo_id="Lab-Rasool/honeybee-samples",
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- filename="sample.PDF",
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- repo_type="dataset",
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- )
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-
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- processor = ClinicalProcessor()
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- result = processor.process(pdf_path)
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- print(result["entities"])
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- ```
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-
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- ### Radiology — Download CT DICOM series
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-
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- ```python
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- from huggingface_hub import snapshot_download
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-
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- ct_dir = snapshot_download(
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- repo_id="Lab-Rasool/honeybee-samples",
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- repo_type="dataset",
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- allow_patterns="CT/**",
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- )
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- ```
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-
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  ## Citation
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  ```bibtex
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- @article{rasool2024honeybee,
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- title={HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models},
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- author={Rasool, Ghulam and others},
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- journal={arXiv preprint arXiv:2405.07460},
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- year={2024}
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- }
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- ```
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-
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- ## License
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-
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- Apache 2.0 — see the [HoneyBee repository](https://github.com/Lab-Rasool/HoneyBee) for details.
 
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  ---
 
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  task_categories:
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  - image-classification
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  - feature-extraction
 
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  └── 1.3.6.1.4.1.14519.5.2.1.6450.4007.2906.../ (104 slices)
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  ```
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  ## Citation
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  ```bibtex
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+ Tripathi, A., Waqas, A., Schabath, M.B. et al. HONeYBEE: enabling scalable multimodal AI in
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+ oncology through foundation model-driven embeddings. npj Digit. Med. 8, 622 (2025).
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+ https://doi.org/10.1038/s41746-025-02003-4
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+ ```