数据集获取

git clone https://github.com/wyim/MEDIQA-M3G-2024.git

curl -L -o repo.zip https://github.com/wyim/MEDIQA-M3G-2024/archive/refs/heads/main.zip
unzip repo.zip

源站 README 摘录（使用方式）

MEDIQA-M3G-2024

• Website: https://sites.google.com/view/mediqa2024/mediqa-m3g
• MEDIQA-M3G@Codabench: https://www.codabench.org/competitions/1632/

Task Description

This dataset includes clinical dermatology textual queries and their associated images, as well as the answers to the queries.
The languages used here are Chinese (zh), English (en), and Spanish (es).

Data Splits

The following table is the number of instances for each split.

Train	Valid	Test
842	56	100

• There will be a Chinese, English, and Spanish version of each.
• All non-Chinese train splits are machine translated from the Chinese original.
• All valid/test sets are human translated and validated by medical doctors.

Data Description

Input Content
(a) json list where each instance will be represented by a json object with the following attributes:

attribute_id	description
encounter_id	unique identification string for the case
image_ids	list of strings of the image_ids
query_title_	a string representing the query title
query_content_	a string representing the query content

(b) image files with unique ids

Images are stored in the following folders respectively:
images_{train,valid,test}/

Image naming convention is as follows: IMG_{ENCOUNTER_ID}_{IMAGENUM}.*

© df_userinfo.csv

csv where each row will include author attributes:

attribute_id	description
author_id	unique identification string for the author
validation_level	validation level of the response author obtained by uploading their professional credentials (categories are: {realid_validated, md_validated, md1_validated, md2_validated, md3_validated, md4_validated}) - md{} - indicates some medical doctor certificate uploaded, the higher the number, the higher the rank. (Note: no validation level doesn’t mean author is not a doctor)
rank_level	author leveling system based on past useful response ratings (level_{0-8}, higher the better)

(d) Reference data will additionally have the field:

attribute_id	description
responses	a list of json objects with the following keys ( author_id, content_{LANGUAGE} , completeness, contains_freq_ans)

completeness: given a score {0.0, 0.5, 1.0}, depending on if the original queries’ question was fully answered. A score of 1.0 indicates

完整仓库：github.com/wyim/MEDIQA-M3G-2024