Datasets:
Add task category and link to paper (#1)
Browse files- Add task category and link to paper (0869aaf7acaade9ec4fefa0ffd49cbc52ae72e22)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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license: apache-2.0
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tags:
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---
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# π€ OCT-Bench
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We introduce OCT-Bench, a comprehensive benchmark for evaluating Multimodal Large Language Models (MLLMs) on optical coherence tomography (OCT) image understanding. OCT-Bench comprises 10,076 expert-verified multiple-choice questions from 4,137 OCT images across seven public datasets and evaluates 3 capability dimensions, 9 capability groups, and 20 fine-grained tasks covering perception, cognition, and clinical reasoning. We benchmark 20 representative MLLMs, including proprietary, open-source, and medical-domain models, providing a comprehensive assessment of OCT understanding capabilities.
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You can download **OCT-Bench**. The expected directory structure is:
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```
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OCT-Bench
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βββ images
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βββ T01_VQA.jsonl
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βββ T02_VQA.jsonl
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βββ ...
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```
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license: apache-2.0
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task_categories:
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- image-text-to-text
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tags:
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- multimodal
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- ophthalmology
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- OCT
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- benchmark
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- medical
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- visual question answering
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# π€ OCT-Bench
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[Paper](https://huggingface.co/papers/2607.16609) | [GitHub](https://github.com/baochenfu/OCT-Bench)
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We introduce OCT-Bench, a comprehensive benchmark for evaluating Multimodal Large Language Models (MLLMs) on optical coherence tomography (OCT) image understanding. OCT-Bench comprises 10,076 expert-verified multiple-choice questions from 4,137 OCT images across seven public datasets and evaluates 3 capability dimensions, 9 capability groups, and 20 fine-grained tasks covering perception, cognition, and clinical reasoning. We benchmark 20 representative MLLMs, including proprietary, open-source, and medical-domain models, providing a comprehensive assessment of OCT understanding capabilities.
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You can download **OCT-Bench**. The expected directory structure is:
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```
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OCT-Bench
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βββ images
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βββ T01_VQA.jsonl
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βββ T02_VQA.jsonl
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βββ ...
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```
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