SLAKE: A Semantically-Labeled Medical Visual Question Answering Dataset in English

SLAKE is a Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering, presented at ISBI 2021. This version, uploaded by mdwiratathya, filters the original bilingual dataset to contain only English entries, providing images as PIL objects, questions, and answers. The dataset was last updated on the Hugging Face platform on June 14, 2024.

Use Cases

• Train medical visual question answering models based on the provided image-question-answer triplets.
• Benchmark multimodal AI systems on semantically-labeled medical knowledge tasks.
• Fine-tune large language models for medical image interpretation based on the question-answer pairs.
• Develop educational tools for medical training using visual question answering.

Strengths

• Dataset is derived from a peer-reviewed publication (ISBI 2021 oral).
• Includes semantic labeling and knowledge enhancement as described in the original work.
• Last updated on 2024-06-14, indicating recent platform activity.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license information are unknown, which may limit suitability assessment.
• Data may reflect bias inherent to the original source collection and filtering process.

Provenance

Source

Original dataset retrieved from https://huggingface.co/datasets/BoKelvin/SLAKE, modified by mdwiratathya.

Collection Method

Filtered from the original bilingual dataset to contain only English entries, taking only image, question, and answer columns.

Time Range

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Freshness

Last updated 2024-06-14 06:28:44; freshness should be verified.

Geography

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