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---
dataset_info:
features:
- name: index
dtype: int64
- name: question
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: E
dtype: string
- name: answer
dtype: string
- name: category
dtype: string
- name: clinical VQA task
dtype: string
- name: department
dtype: string
- name: perceptual granularity
dtype: string
- name: modality
dtype: string
- name: original task
dtype: string
- name: image_path
dtype: string
splits:
- name: validation
num_bytes: 72470811
num_examples: 10
download_size: 72466952
dataset_size: 72470811
task_categories:
- visual-question-answering
language:
- en
tags:
- medical
Modalities:
- image
- text
arxiv:
- https://arxiv.org/abs/2507.17539
license: creativeml-openrail-m
---
# Fundus-MMBench
Benchmark for paper [Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning](https://arxiv.org/abs/2507.17539)
🚨 Important: This benchmark is for **academic research only**.
## Dataset Viewer Notice
🚨 The dataset viewer above only shows a **preview of the first 10 rows** from the dataset intending to provide a quick look at the data structure.
The total number of data in the dataset is **620**.
To access the complete dataset, please download the full tsv file.
## Introduction
<img src="image1.png" alt="Fundus-MMBench Details" width="1000" />
The number of test samples for each task category of Fundus-MMBench is 20. It consists of 31 fine-grained tasks covering three core clinical domains: region-based object recognition (e.g., optic disc identification), disease classification (e.g., glaucoma vs. non-glaucoma diagnosis), and severity grading (e.g., diabetic retinopathy severity assessment).
## Usage
You can run the evaluation on Fundus-MMBench using [open-compass/VLMEvalKit](https://github.com/open-compass/VLMEvalKit). Note that Fundus-MMBench(tsv version) is not officially supported, but can be regarded as a Custom MCQ dataset.
## Data Source
| Dataset Name | Data Source URL |
| :--- | :--- |
| Diaretdb1 | https://www.kaggle.com/datasets/nguyenhung1903/diaretdb1-v21 |
| drishtiGS | https://www.kaggle.com/datasets/lokeshsaipureddi/drishtigs-retina-dataset-for-onh-segmentation |
| IDRiD | https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid |
| REFUGE | https://refuge.grand-challenge.org/REFUGE2018/ |
| e-ophtha | https://www.adcis.net/en/third-party/e-ophtha/ |
| Naikai OIA-DDR | https://github.com/nkicsl/DDR-dataset |
| ROC training | https://roc.grand-challenge.org/ |
| BRSET | https://physionet.org/content/brazilian-ophthalmological/1.0.0/ |
| PALM | https://ieee-dataport.org/documents/palm-pathologic-myopia-challenge |
| Glaucoma_fundus | https://drive.google.com/file/d/18vSazOYDsUGdZ64gGkTg3E6jiNtcrUrI/view |
| PAPILA | https://drive.google.com/file/d/1JltYs7WRWEU0yyki1CQw5-10HEbqCMBE/view |
| Retina | https://drive.google.com/file/d/1vdmjMRDoUm9yk83HMArLiPcLDk_dm92Q/view |
| JSIEC | https://drive.google.com/file/d/1q0GFQb-dYwzIx8AwlaFZenUJItix4s8z/view |
| MESSIDOR2 | https://drive.google.com/file/d/1vOLBUK9xdzNV8eVkRjVdNrRwhPfaOmda/view |
| APTOS2019 | https://drive.google.com/file/d/162YPf4OhMVxj9TrQH0GnJv0n7z7gJWpj/view |
| In-house | |
We would like to thank these contributions.