Datasets:
				
			
			
	
			
			
	
		
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            license: cc-by-sa-3.0
         
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            tags:
         
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            - medical
         
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            language:
         
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            - en
         
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            task_categories:
         
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              data_files:
         
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              - split: test
         
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                path: EndoBench.tsv
         
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            ---
         
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            # <div align="center"><b> EndoBench </b></div>
         
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            This repository is the official implementation of the paper **[EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis](https://huggingface.co/papers/2505.23601)**.
         
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            ## π News
         
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            - [19/09/2025] ππOur EndoBench was accepted by NeurIPS'25 D&B Track!!!
         
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            ## βοΈ Tutorial 
         
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            3. You can find more details on the [ImageMCQDataset Class](https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/image_mcq.py).
         
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            ## Disclaimers
         
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            The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution.
         
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            Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to contact us. Upon verification, such samples will be promptly removed.
         
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            -
            Greatly appreciate to all the authors of these datasets for their contributions to the field of endoscopy analysis.
         
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            license: cc-by-sa-3.0
         
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            tags:
         
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            - medical
         
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            - endoscopy
         
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            - VQA
         
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            - VLM
         
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            language:
         
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            - en
         
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            task_categories:
         
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              data_files:
         
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              - split: test
         
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                path: EndoBench.tsv
         
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            pretty_name: EndoBench
         
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            size_categories:
         
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            - 1K<n<10K
         
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            ---
         
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            # <div align="center"><b> EndoBench </b></div>
         
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            This repository is the official implementation of the paper **[EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis](https://huggingface.co/papers/2505.23601)**.
         
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            ## π News
         
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            - [21/10/2025] We release a new open-set challenging VQA benchmark [EndoBench-extended](https://huggingface.co/datasets/Saint-lsy/EndoBench-Extended).
         
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            - [19/09/2025] ππOur EndoBench was accepted by NeurIPS'25 D&B Track!!!
         
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            ## βοΈ Tutorial 
         
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            3. You can find more details on the [ImageMCQDataset Class](https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/image_mcq.py).
         
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            ## π₯ EndoBench-Extended
         
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            This [EndoBench-extended](https://huggingface.co/datasets/Saint-lsy/EndoBench-Extended) dataset is the extended version of Endobench, which includes 48 images and 91 open-set VQA pairs, carefully selected by medical experts to ensure clinical relevance and diagnostic difficulty.  The EndoBench-extended specifically focuses on challenging and underrepresented cases, including rare pathologies, overlapping lesions, atypical anatomical presentations, and post-surgical endoscopic views.
         
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            ## Disclaimers
         
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            The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution.
         
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            Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to contact us. Upon verification, such samples will be promptly removed.
         
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            Greatly appreciate to all the authors of these datasets for their contributions to the field of endoscopy analysis.
         
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