Package: hi-ml-multimodal
HI-ML Multimodal ToolboxThis toolbox provides models for multimodal health data.
The code is available onGitHubandHugging Face 🤗.Getting startedThe best way to get started is by running thephrase grounding notebookand theexamples.
All the dependencies will be installed upon execution, so Python 3.9 andJupyterare the only requirements to get started.The notebook can also be run onBinder, without the need to download any code or install any libraries:InstallationThe latest version can be installed usingpip:pip install --upgrade hi-ml-multimodalDevelopmentFor development, it is recommended to clone the repository and set up the environment usingconda:git clone https://github.com/microsoft/hi-ml.gitcd hi-ml-multimodalmake envThis will create acondaenvironment namedmultimodaland install all the dependencies to run and test the package.You can visit theAPI documentationfor a deeper understanding of our tools.ExamplesFor zero-shot classification of images using text prompts, please refer to theexample scriptthat utilises a small subset ofOpen-Indiana CXR
datasetfor pneumonia detection in chest X-ray images.
Please note that the examples and models are not intended for deployed use cases (commercial or otherwise), which is currently out-of-scope.Hugging Face 🤗While theGitHub repositoryprovides examples and pipelines to use our models,
the weights and model cards are hosted onHugging Face 🤗.CreditIf you use our code or models in your research, please cite our recent ECCV and CVPR papers:Boecking, B., Usuyama, N. et al. (2022).Making the Most of Text Semantics to Improve Biomedical Vision–Language Processing. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13696. Springer, Cham.https://doi.org/10.1007/978-3-031-20059-5_1Bannur, S., Hyland, S., et al. (2023).Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing. In: CVPR 2023.BibTeX@InProceedings{10.1007/978-3-031-20059-5_1,author="Boecking, Benedikt and Usuyama, Naoto and Bannur, Shruthi and Castro, Daniel C. and Schwaighofer, Anton and Hyland, Stephanie and Wetscherek, Maria and Naumann, Tristan and Nori, Aditya and Alvarez-Valle, Javier and Poon, Hoifung and Oktay, Ozan",editor="Avidan, Shai and Brostow, Gabriel and Ciss{\'e}, Moustapha and Farinella, Giovanni Maria and Hassner, Tal",title="Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing",booktitle="Computer Vision -- ECCV 2022",year="2022",publisher="Springer Nature Switzerland",address="Cham",pages="1--21",isbn="978-3-031-20059-5"}@inproceedings{bannur2023learning,title={Learning to Exploit Temporal Structure for Biomedical Vision{\textendash}Language Processing},author={Shruthi Bannur and Stephanie Hyland and Qianchu Liu and Fernando P\'{e}rez-Garc\'{i}a and Maximilian Ilse and Daniel C. Castro and Benedikt Boecking and Harshita Sharma and Kenza Bouzid and Anja Thieme and Anton Schwaighofer and Maria Wetscherek and Matthew P. Lungren and Aditya Nori and Javier Alvarez-Valle and Ozan Oktay},booktitle={Conference on Computer Vision and Pattern Recognition 2023},year={2023},url={https://openreview.net/forum?id=5jScn5xsbo}}