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@@ -16,23 +16,18 @@ PubMedCLIP is a fine-tuned version of [CLIP](https://huggingface.co/docs/transfo
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  image–text pairs obtained from [PubMed](https://pubmed.ncbi.nlm.nih.gov/) articles.
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  ## Model Details
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- PubMedCLIP was trained on the [Radiology Objects in COntext (ROCO)](https://link.springer.com/chapter/10.1007/978-3-030-01364-6_20) dataset, which provides
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- over 80,000 samples including diverse imaging modalities (such as ultrasound, X-Ray, MRI, etc.) from various human body regions (such as head, neck, spine, etc.)
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- captured from PubMed articles. The texts used for training were taken from the short captions (average length of 20 words) associated with images in the articles.
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  ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- - **Developed by:** {{ developers | default("[More Information Needed]", true)}}
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- - **Model type:** {{ model_type | default("[More Information Needed]", true)}}
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- - **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
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- - **License:** {{ license | default("[More Information Needed]", true)}}
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- - **Finetuned from model [optional]:** {{ finetuned_from | default("[More Information Needed]", true)}}
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- ### Model Sources [optional]
 
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** {{ repo | default("[More Information Needed]", true)}}
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  - **Paper [optional]:** {{ paper | default("[More Information Needed]", true)}}
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  - **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
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  image–text pairs obtained from [PubMed](https://pubmed.ncbi.nlm.nih.gov/) articles.
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  ## Model Details
 
 
 
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  ### Model Description
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+ PubMedCLIP was trained on the [Radiology Objects in COntext (ROCO)](https://github.com/razorx89/roco-dataset) dataset, a large-scale multimodal medical imaging dataset.
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+ The ROCO dataset includes diverse imaging modalities (such as ultrasound, X-Ray, MRI, etc.) from various human body regions (such as head, neck, spine, etc.)
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+ captured from open-access PubMed articles. The texts used for training PubMedCLIP were taken from the short captions associated with the images in the dataset.
 
 
 
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+ The authors of PubMedCLIP have released three different pre-trained models at this [link](https://1drv.ms/u/s!ApXgPqe9kykTgwD4Np3-f7ODAot8?e=zLVlJ2) using
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+ ResNet-50, ResNet-50x4 and ViT32 as image encoders. This repository includes only the ViT32 variant.
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+ ### Model Sources
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+ - **Repository:** [Official GitHub Repository](https://github.com/sarahESL/PubMedCLIP)
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  - **Paper [optional]:** {{ paper | default("[More Information Needed]", true)}}
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  - **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
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