--- license: cc-by-nc-sa-4.0 language: - en tags: - medical-imaging - ct-scan - 3d - vision-transformer - self-supervised-learning - foundation-model - radiology library_name: transformers pipeline_tag: feature-extraction --- πŸ“’ [2026-05-20] The pretrained SPECTRE model can now be loaded directly through the `transformers` library, no separate SPECTRE package installation required. Check below for details and usage examples. πŸ“’ [2026-04-10] SPECTRE is now an official baseline for the [**CVPR 2026 Workshop Competition: Foundation Models for General CT Image Diagnosis**](https://www.codabench.org/competitions/12650/)! See `experiments/cvpr26_fm_for_ct_diag_task_1` for scripts and additional details. πŸ“’ [2026-02-21] SPECTRE has been accepted for presentation at **CVPR 2026** (Denver, Colorado, USA)! πŸ“’ [2026-01-20] [Semantic segmentation](https://github.com/cviviers/nnUNet) code and configurations using the nnUNet framework are now released! # SPECTRE πŸ‘»πŸ‘»πŸ‘»

PyPI Version Python Versions Downloads per Month License Model weights Preprint

SPECTRE architecture and pretraining strategies

SPECTRE (**S**elf-Supervised & Cross-Modal **P**r**e**training for **CT** **R**epresentation **E**xtraction) is a **Transformer-based foundation model for 3D Computed Tomography (CT) scans**, trained using **self-supervised learning** (SSL) and **cross-modal vision–language alignment** (VLA). It provides rich and generalizable representations from medical imaging data, which can be fine-tuned for downstream tasks such as segmentation, classification, and anomaly detection. SPECTRE has been trained on a large cohort of **open-source CT scans** of the **human abdomen and thorax**, as well as **paired radiology reports** and **Electronic Health Record data**, enabling it to capture representations that generalize across datasets and clinical settings. This repository provides pretrained SPECTRE models together with tools for fine-tuning and evaluation. ## 🧠 Pretrained Models The pretrained SPECTRE model can easily be imported using the `transformers` library ```python from transformers import AutoModel model = AutoModel.from_pretrained('cclaess/SPECTRE-Large', trust_remote_code=True) ``` or by using the `spectre-fm` package as follows: ```python from spectre import SpectreImageFeatureExtractor, MODEL_CONFIGS config = MODEL_CONFIGS['spectre-large-pretrained'] model = SpectreImageFeatureExtractor.from_config(config) ``` A simple forward pass would look like: ```python import torch model.eval() # Dummy input: (batch, crops, channels, height, width, depth) # For a (3 x 3 x 4) grid of (128 x 128 x 64) CT patches -> Total scan size (384 x 384 x 256) x = torch.randn(1, 1, 384, 384, 256) B, C, H, W, D = x.shape patch_size = (128, 128, 64) pH, pW, pD = patch_size x = x.view( B, C, H // pH, pH, W // pW, pW, D // pD, pD, ).permute(0, 2, 4, 6, 1, 3, 5, 7).reshape(B, -1, C, pH, pW, pD) with torch.no_grad(): features = model( x, grid_size=( H // pH, W // pW, D // pD, ), ) print("Features shape:", features.shape) ``` Alternatively, you can download the weights of the separate components through HuggingFace using the following links: | Architecture | Input Modality | Pretraining Objective | Model Weights | |---------------------------|--------------------|-------------------------|-----------------------------------------------------------------------------------------------------------------------------| | SPECTRE-ViT-Local | CT crops | SSL | [Link](https://huggingface.co/cclaess/SPECTRE/resolve/main/spectre_backbone_vit_large_patch16_128_no_vla.pt?download=true) | | SPECTRE-ViT-Local | CT crops | SSL + VLA | [Link](https://huggingface.co/cclaess/SPECTRE/resolve/main/spectre_backbone_vit_large_patch16_128.pt?download=true) | | SPECTRE-ViT-Global | Embedded CT crops | VLA | [Link](https://huggingface.co/cclaess/SPECTRE/resolve/main/spectre_combiner_feature_vit_large.pt?download=true) | | Qwen3-Embedding-0.6B LoRA | Text (radiology) | VLA | [Link](https://huggingface.co/cclaess/SPECTRE/resolve/main/spectre_qwen3_embedding_0.6B_lora.pt?download=true) | ## 🩻 Segmentation (nnUNet) If you're looking for a nnUNet-based segmentation pipeline that uses SPECTRE as the backbone, see: https://github.com/cviviers/nnUNet ## πŸ“‚ Repository Contents This repository is organized as follows: - πŸš€ **`src/spectre/`** – Contains the core package, including: - Pretraining methods - Model architectures - Data handling and transformations - πŸ› οΈ **`src/spectre/configs/`** – Stores configuration files for different training settings. - πŸ”¬ **`experiments/`** – Includes Python scripts for running various pretraining and downstream experiments. - 🐳 **`Dockerfile`** – Defines the environment for running a local version of SPECTRE inside a container. ## βš™οΈ Setting Up the Environment To get up and running with SPECTRE, install the base package with pip: ```bash pip install spectre-fm ``` This installs only the runtime dependencies needed to load and run the pretrained models. If you want to fine-tune or pretrain SPECTRE, install the matching extra: ```bash pip install "spectre-fm[training]" ``` If you only need the evaluation stack, install: ```bash pip install "spectre-fm[eval]" ``` If training on GDS-enabled systems is required, install the CUDA 12 specific extra: ```bash pip install "spectre-fm[gds-cuda12]" # with training stack: "spectre-fm[training,gds-cuda12]" ``` **Note that** `gds-cuda12` is only compatible with CUDA 12.x environments. To install everything at once, use: ```bash pip install "spectre-fm[all]" ``` or install the latest updates directly from GitHub: ```bash pip install git+https://github.com/cclaess/SPECTRE.git ``` ## 🐳 Building and Using Docker To facilitate deployment and reproducibility, SPECTRE can be run using **Docker**. This allows you to set up a fully functional environment without manually installing dependencies using your own local copy of spectre. ### **Building the Docker Image** First, ensure you have **Docker** installed. Then, clone and navigate to the repository to build the image: ```bash git clone https://github.com/cclaess/SPECTRE cd SPECTRE docker build -t spectre-fm . ``` ### **Running Experiments Inside Docker** Once the image is built, you can start a container and execute scripts inside it. For example, to run a DINO pretraining experiment: ```bash docker run --gpus all --rm -v "$(pwd):/mnt" spectre-fm python3 experiments/pretraining/pretrain_dino.py --config_file spectre/configs/dino_default.yaml --output_dir /mnt/outputs/pretraining/dino/ ``` - `--gpus all` enables GPU acceleration if available. - `--rm` removes the container after execution. - `-v $(pwd):/mnt` mounts the current directory inside the container. ## βš–οΈ License - **Code: MIT** β€” see `LICENSE` (permissive; commercial use permitted). - **Pretrained model weights: CC-BY-NC-SA** β€” non-commercial share-alike. The weights and any derivative models that include these weights are NOT cleared for commercial use. See `LICENSE_MODELS` for details and the precise license text. > Note: the pretrained weights are subject to the original dataset licenses. Users intending to use SPECTRE in commercial settings should verify dataset and model licensing and obtain any required permissions. ## πŸ“œ Citation If you use SPECTRE in your research or wish to cite it, please use the following BibTeX entry of our [preprint](https://arxiv.org/abs/2511.17209): ``` @misc{claessens_scaling_2025, title = {Scaling {Self}-{Supervised} and {Cross}-{Modal} {Pretraining} for {Volumetric} {CT} {Transformers}}, url = {http://arxiv.org/abs/2511.17209}, doi = {10.48550/arXiv.2511.17209}, author = {Claessens, Cris and Viviers, Christiaan and D'Amicantonio, Giacomo and Bondarev, Egor and Sommen, Fons van der}, year={2025}, } ``` ## 🀝 Acknowledgements This project builds upon prior work in self-supervised learning, medical imaging, and transformer-based representation learning. We especially acknowledge [**MONAI**](https://project-monai.github.io/) for their awesome framework and the [**timm**](https://timm.fast.ai/) & [**lightly**](https://docs.lightly.ai/self-supervised-learning/) Python libraries for providing 2D PyTorch models (timm) and object-oriented self-supervised learning methods (lightly), from which we adapted parts of the code for 3D. [![Star History Chart](https://api.star-history.com/svg?repos=cclaess/SPECTRE&type=Date)](https://star-history.com/#cclaess/SPECTRE&Date)