--- license: other license_name: license license_link: LICENSE tags: - medical - radiology - swin-unetr --- **CoralBay**: A Self-Supervised CT Foundation Model Quick Highlights - **True 3D CT**: Moves beyond 2D slice models to understand full volumetric anatomy and spatial relationships. - **Data Efficient**: Top-tier classification and segmentation performance from just 11K unlabeled CT scans. - **One Model, Many Tasks**: From fine-grained analysis to global understanding and downstream applications. - **Open & Ready for Impact**: Open-source, CT task robust, and built to accelerate the future of medical AI. ### Quick Start ```py3 import urllib.request import torch from monai import transforms, inferers from transformers import AutoModel # 1. Download sample CT scan url = "https://github.com/neurolabusc/niivue-images/raw/refs/heads/main/CT_Abdo.nii.gz" urllib.request.urlretrieve(url, "CT_Abdo.nii.gz") # 2. Preprocess volume preprocess = transforms.Compose([ transforms.LoadImage(image_only=True), transforms.EnsureChannelFirst(), transforms.Spacing( pixdim=(1.5, 1.5, 1.5), mode="bilinear" ), # dev-only crop for speed transforms.CenterSpatialCrop(roi_size=192), # any HU window (a_min, a_max) will do transforms.ScaleIntensityRange( a_min=-1000, a_max=1000, b_min=0.0, b_max=1.0, clip=True, ), ]) x = preprocess("CT_Abdo.nii.gz").float().unsqueeze(0) # (1, 1, D, H, W) # 3. Load model device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModel.from_pretrained( "kaiko-ai/coralbay", trust_remote_code=True, out_indices=None, # out_indices=6, # = None for the aggregated feature vector ).eval().to(device) # 4. Attach sliding-window inference model.encoder._inferer = inferers.SlidingWindowInferer( roi_size=(96, 96, 96), sw_batch_size=2, overlap=0.0, # 0.75 for better results mode="gaussian", sw_device=device, # run windows on GPU device="cpu", # stitch results on CPU ) # 5. Run inference with torch.no_grad(): features = model(x.to(device)) # 6. Output # for `out_indices=6`: # features is a multi-scale feature pyramid: # [0] (1, 192, 96, 96, 96) # [1] (1, 192, 48, 48, 48) # [2] (1, 384, 24, 24, 24) # [3] (1, 768, 12, 12, 12) # [4] (1, 1536, 6, 6, 6) # [5] (1, 3072, 3, 3, 3) # for `out_indices=None`: # features is a tensor 1, 4608) ``` ### Quantitative Performance Quantitative performance across classification (Multiclass Accuracy/Binary AUROC) and segmentation (Dice score) tasks, as evaluated via the [eva](https://github.com/kaiko-ai/eva) framework. ## Citation If you use this model, please cite it as follows: ```bibtex @misc{gatopoulos2026coralbayselfsupervisedctfoundation, title={CoralBay: A Self-Supervised CT Foundation Model}, author={Ioannis Gatopoulos and Nicolas Känzig and Sebastian Otálora and Fei Tang}, year={2026}, eprint={2606.03888}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2606.03888}, } ```
