Most medical imaging AI today is narrowly built to detect a small set of individual findings on a single modality like chest X-rays. This training approach is data- and computationally inefficient, requiring ~6-12 months per finding1, and often fails to generalize in real world environments. By further training existing multimodal foundation models on medical images and associated text data, Microsoft and Nuance created a multimodal foundation model that shows evidence of generalizing across various medical imaging modalities, anatomies, locations, severities, and types of medical data. The training methods learn to map the medical text and images into a unified numerical vector representation space, which makes it easy for computers to understand the relationships between those modalities.

Embeddings are an important building block in AI research and development for retrieval, search, comparison, classification, and tagging tasks, and developers and researchers can now use MedImageInsight embeddings in the medical domain. MedImageInsight embeddings is open source allowing developers to customize and adapt to their specific use cases.

This repository contains the MedImageInsight model, which is packaged in MLflow format and deployed using Azure ML service. The estimated time to package and deploy the model is approximately 1 hour.

This model is intended and provided as-is for research and model development exploration. MedImageInsight is not designed or intended to be deployed in clinical settings as-is nor is it for use in the diagnosis or treatment of any health or medical condition, and the model’s performance for such purposes has not been established.

You bear sole responsibility and liability for any use of MedImageInsight, including verification of outputs and incorporation into any product or service intended for a medical purpose or to inform clinical decision-making, compliance with applicable healthcare laws and regulations, and obtaining any necessary clearances or approvals.

Please see https://aka.ms/medimageinsightpaper for more details.

For documentation and example Jupyter Notebooks, visit: https://aka.ms/MedImageInsightDocs.

[^1]: 2022.12.07.22283216v3.full.pdf (medrxiv.org)

Microsoft MedImageInsight includes 360 million parameter image encoder and 252 million parameter language encoder and comes as pretrained model with fine-tuning capability. The language encoder is not run in inference for each image. It is only run once (offline) to generate classifier head. MedImageInsight is a vision language transformer and was derived from the Florence computer vision foundation model. Florence is a two-tower architecture similar to CLIP, except the DaViT architecture is used as the image encoder and the UniCL objective is used as the objective function for MedImageInsight.

Model input supports image and text input and generates vector embeddings as output. This is a static model trained on an offline dataset that is described below.

The license for MedImageParse is the MIT license. For questions or comments, please contact: [email protected]

In this section, we report the results for the models on standard academic benchmarks. For all the evaluations, we use our internal evaluations library. For these models, we always pick the best score between our evaluation framework and any publicly reported results. Full details at https://aka.ms/medimageinsightpaper

*SOTA for this task

The table below highlights the performance (AUC) of Bone Age prediction and ChextX-ray text search tasks for female and male respectively.

The table below highlight characterisitcs of patients whose OCT images were included in the analysis.

We plan on doing more comprehensive fairness evaluations before public release.

Microsoft believes Responsible AI is a shared responsibility and we have identified six principles and practices help organizations address risks, innovate, and create value: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant use case and addresses unforeseen product misuse. 

While testing the model with images and/or text, ensure the the data is PHI free and that there are no patient information or information that can be tracked to a patient identity.

The model is not designed for the following use cases:

• Use by clinicians to inform clinical decision-making, as a diagnostic tool, or as a medical device - MedImageInsight is not designed or intended to be deployed as-is in clinical settings nor is it for use in the diagnosis, cure, mitigation, treatment, or prevention of disease or other conditions (including to support clinical decision-making), or as a substitute of professional medical advice, diagnosis, treatment, or clinical judgment of a healthcare professional.    

• Scenarios without consent for data - Any scenario that uses health data for a purpose for which consent was not obtained.  

• Use outside of health scenarios - Any scenario that uses non-medical related image and/or serving purposes outside of the healthcare domain. 

Please see Microsoft's Responsible AI Principles and approach available at https://www.microsoft.com/en-us/ai/principles-and-approach/

Input:

data =  {
  "input_data": {
    "columns": [
      "image",
      "text"
    ],
    "index":[0],
    "data": [
      [base64.encodebytes(read_image(sample_image_1)).decode("utf-8"), "x-ray chest anteroposterior Cardiomegaly"]
    ]
  },
  "params":{
      "get_scaling_factor": True
  }
}

Output:

[
  {
    "image_features": [
      [-0.040428221225738525, 0.015632804483175278, -0.034625787287950516, -0.013094332069158554, ... , 0.023215821012854576, -0.010303247720003128, -0.003998206462711096, -0.00022746287868358195]
    ]
  },
  {
    "text_features": [
      [-0.04121647855639458, 0.014923677921295166, -0.033598374396562576, -0.012765488520264626, ... ,  0.02294582130014801, -0.009835227608680725, -0.004232016112744808, -0.00021812367581298325]
    ]
  },
  {
    "scaling_factor": 4.513362407684326
  }
]

• Supports CPU and GPU
• Default: Single V100 GPU or Intel CPU
• Minimum: Single GPU instance with 8Gb Memory (Fastest) or CPU

Version: 4

task : embeddings industry : health-and-life-sciences Preview inference_supported_envs : ['hf'] license : mit author : Microsoft hiddenlayerscanned SharedComputeCapacityEnabled inference_compute_allow_list : ['Standard_NC6s_v3', 'Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_NC24ads_A100_v4', 'Standard_NC48ads_A100_v4', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4', 'Standard_ND40rs_v2']

View in Studio: https://ml.azure.com/registries/azureml/models/MedImageInsight/version/4

License: mit

inference-min-sku-spec: 6|1|112|64

inference-recommended-sku: Standard_NC6s_v3, Standard_NC12s_v3, Standard_NC24s_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2

languages: en

SharedComputeCapacityEnabled: True