metadata

license: mit
language:
  - en
tags:
  - medical
  - vision
widget:
  - src: https://d168r5mdg5gtkq.cloudfront.net/medpix/img/full/synpic9078.jpg
    candidate_labels: Chest X-Ray, Brain MRI, Abdomen CT Scan
    example_title: Abdomen CT Scan

Model Card for PubMedCLIP

PubMedCLIP is a fine-tuned version of CLIP for the medical domain trained on a large number of medical image–text pairs obtained from PubMed articles.

Model Details

PubMedCLIP was trained on the Radiology Objects in COntext (ROCO) dataset, which provides over 80,000 samples including diverse imaging modalities (such as ultrasound, X-Ray, MRI, etc.) from various human body regions (such as head, neck, spine, etc.) captured from PubMed articles. The texts used for training were taken from the short captions (average length of 20 words) associated with images in the articles.

Model Description

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Model Sources [optional]

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Uses

Direct Use

Downstream Use [optional]

Out-of-Scope Use

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Bias, Risks, and Limitations

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How to Get Started with the Model

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Training Details

Training Data

Training Procedure

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Metrics

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Results

Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Hardware

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Citation [optional]

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APA:

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