Zero-Shot Image Classification
Transformers
English
medical
multimodal
vision-language pre-training
chest x-ray
Instructions to use pykale/MeDSLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pykale/MeDSLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="pykale/MeDSLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pykale/MeDSLIP", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# MeDSLIP: Medical Dual-Stream Language-Image Pre-training
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## Introduction:
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## Reference
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```
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@article{fan2024medslip,
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title={MeDSLIP: Medical Dual-Stream Language-Image Pre-training
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author={Fan, Wenrui and Suvon, Mohammod Naimul Islam and Zhou, Shuo and Liu, Xianyuan and Alabed, Samer and Osmani, Venet and Swift, Andrew and Chen, Chen and Lu, Haiping},
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journal={arXiv preprint arXiv:2403.10635},
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year={2024}
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# MeDSLIP: Medical Dual-Stream Language-Image Pre-training with Pathology-Anatomy Semantic Alignment
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## Introduction:
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## Reference
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```
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@article{fan2024medslip,
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title={MeDSLIP: Medical Dual-Stream Language-Image Pre-training with Pathology-Anatomy Semantic Alignment},
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author={Fan, Wenrui and Suvon, Mohammod Naimul Islam and Zhou, Shuo and Liu, Xianyuan and Alabed, Samer and Osmani, Venet and Swift, Andrew and Chen, Chen and Lu, Haiping},
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journal={arXiv preprint arXiv:2403.10635},
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year={2024}
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