Colipri

--- license: mit language: - en library_name: colipri pipeline_tag: zero-shot-image-classification --- # COLIPRI <!-- Provide a quick summary of what the model is/does. --> COLIPRI is a 3D vision&ndash;language transformer model trained to encode chest CT scans and reports. ## Model description <!-- Provide a longer summary of what this model is. --> COLIPRI was trained using tens of thousands of chest CT scans and reports, without any annotations, using multiple objectives to learn strong joint representations of 3D images and text. The procedure is described in detail in our manuscript, [_Comprehensive language-image pre-training for 3D medical image understanding_](https://arxiv.org/abs/2510.15042) (Wald et al. 2026). The weights shared here correspond to our best-performing model, COLIPRI-CRM. - **Developed by:** Microsoft Health Futures - **Model type:** 3D vision&ndash;language encoder - **License:** [MIT](./LICENSE) ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> COLIPRI is shared for research purposes only. It is **not meant to be used for clinical practice**. The encoders be plugged to other models, or used independently or jointly for many downstream tasks, such as: - Image classification with text prompts - Image clustering - Text clustering - Text-to-image retrieval - Image-to-image retrieval - Image-to-text retrieval - Text-to-text retrieval - Image classification with a classifier - Text classification with a classifier - Image segmentation with a decoder - Report generation with a language decoder Fine-tuning COLIPRI is typically not necessary to obtain good performance in downstream tasks. ## Getting started ### Installation ```shell pip install colipri ``` ### Usage examples Below we share some usage snippets to get started with COLIPRI. A more complete [Jupyter notebook](./COLIPRI_demo.ipynb) is also available. First, let's get a 3D chest CT we can use for demonstration. The plotted slices intersect a lung nodule near the heart. ```python >>> from colipri import load_sample_ct >>> image = load_sample_ct() >>> image ScalarImage(shape: (1, 512, 512, 139); spacing: (0.76, 0.76, 2.50); orientation: LPS+; dtype: torch.IntTensor; memory: 139.0 MiB) ``` The image looks like this:  Now...

Source context: 3940 downloads · 54 likes · Pipeline zero-shot-image-classification · Library colipri · Repo microsoft/colipri