docs(tap-hf): Update README and processor config

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. 
 
@@ -53,6 +54,14 @@ with torch.no_grad():
 
 ### Usage with Preprocessor, loading CT volumes & slice-wise inference
 
+**Recommended environment:**
+- Python >= 3.11
+- torch >= 2.8
+- numpy >= 2.35
+- SimpleITK >= 2.52
+- monai >= 1.4.0
+- xformers >= 0.0.32 (optional, recommended for CUDA)
+
 ```python
 import numpy as np
 import SimpleITK as sitk
@@ -69,7 +78,7 @@ volume = sitk.DICOMOrient(volume, 'LPS')
 
 # Get array, expand to (B, C, D, H, W) and preprocess
 array = sitk.GetArrayFromImage(volume)
-array = np.expand_dims(array, axis(0, 1))
+array = np.expand_dims(array, axis=(0, 1))
 x = preprocessor(array)['pixel_values']
 
 # Forward pass
@@ -104,3 +113,16 @@ The model returns a `BaseModelOutputWithPooling` object from the transformers li
 - **Input Shape**: `(batch_size, 1, height, width)`
 - **Example Input**: `(16, 1, 224, 224)` - batch of 16 CT slices at 224×224 resolution
 - **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}
+}
+```

preprocessor_config.json

@@ -1,5 +1,6 @@
 {
     "image_processor_type": "TAPCTProcessor",
+    "use_fast": false,
     "resize_dims": [224, 224],
     "divisible_pad_z": 1,
     "clip_range": [-1008.0, 822.0],