docs(tap-hf): Update README

README.md

@@ -3,6 +3,7 @@ license: cc-by-nc-4.0
 ---
 
 # TAP-CT: 3D Task-Agnostic Pretraining of CT Foundation Models
+[![arXiv](https://img.shields.io/badge/arXiv-TAP--CT-b31b1b.svg)](https://arxiv.org/abs/2512.00872)
 
 TAP-CT is a suite of foundation models for computed tomography (CT) imaging, pretrained in a task-agnostic manner through an adaptation of DINOv2 for volumetric data. These models learn robust 3D representations from CT scans without requiring task-specific annotations. 
 
@@ -60,6 +61,7 @@ with torch.no_grad():
 - numpy >= 2.35
 - SimpleITK >= 2.52
 - monai >= 1.4.0
+- xformers >= 0.0.32 (optional, recommended for CUDA)
 
 ```python
 import numpy as np
@@ -112,4 +114,17 @@ The model returns a `BaseModelOutputWithPooling` object from the transformers li
 - **Model Type**: 3D CT Vision Foundation Model
 - **Input Shape**: `(batch_size, 1, depth, height, width)`
 - **Example Input**: `(16, 1, 12, 224, 224)` - batch of 16 CT crops with 12 slices at 224×224 resolution
-- **License**: CC-BY-NC-4.0
+- **License**: CC-BY-NC-4.0
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{veenboer2025tapct,
+  title={TAP-CT: 3D Task-Agnostic Pretraining of Computed Tomography Foundation Models},
+  author={Veenboer, Tim and Yiasemis, George and Marcus, Eric and Van Veldhuizen, Vivien and Snoek, Cees G. M. and Teuwen, Jonas and Groot Lipman, Kevin B. W.},
+  journal={arXiv preprint arXiv:2512.00872},
+  year={2025}
+}
+```