Update README.md

changed textual description of license to CC-BY-NC-4.0

README.md

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----
-license: cc-by-nc-4.0
----
-
-# TAP-CT: 3D Task-Agnostic Pretraining of CT Foundation Models
-
-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. 
-
-This repository provides TAP-CT-B-3D, a Vision Transformer (ViT-Base) architecture pretrained on volumetric inputs with a spatial resolution of (12, 224, 224) and a patch size of (4, 8, 8). For inference on full-resolution CT volumes, a sliding window approach can be employed to extract features across the entire scan. Additional TAP-CT model variants, as well as the image processor, will be released in future updates.
-
-## Preprocessing
-
-While a dedicated image processor will be released in future updates, optimal feature extraction requires the following preprocessing pipeline:
-
-1. **Orientation**: Convert the volume to LPS (Left-Posterior-Superior) orientation. While the model is likely orientation-invariant, all evaluations were conducted using LPS orientation.
-2. **Spatial Resizing**: Resize the volume to a spatial resolution of \(z, 224, 224\) or \(z, 512, 512\), where \(z\) represents the number of slices along the axial dimension.
-3. **Axial Padding**: Apply zero-padding along the \(z\)-axis to ensure divisibility by 4, accommodating the model's patch size of (4, 8, 8).
-4. **Intensity Clipping**: Clip voxel intensities to the range \([-1008.0, 822.0]\) HU (Hounsfield Units).
-5. **Normalization**: Apply z-score normalization using \(mean = -86.8086\) and \(std = 322.6347\).
-
-## Usage
-
-```python
-import torch
-from transformers import AutoModel
-
-# Load the model
-model = AutoModel.from_pretrained('nki-radiology/tap-ct-b-3d', trust_remote_code=True)
-
-# Prepare input (batch_size, channels, depth, height, width)
-x = torch.randn((16, 1, 12, 224, 224))
-
-# Forward pass
-output = model.forward(x)
-```
-
-The model returns a `BaseModelOutputWithPooling` object from the transformers library. The `output.pooler_output` contains the pooled `[CLS]` token representation, while `output.last_hidden_state` contains the spatial patch token embeddings. To extract features from all intermediate transformer layers, pass `output_hidden_states=True` to the forward method.
-
-## Model Details
-
-- **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**: Apache 2.0
+---
+license: cc-by-nc-4.0
+---
+
+# TAP-CT: 3D Task-Agnostic Pretraining of CT Foundation Models
+
+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. 
+
+This repository provides TAP-CT-B-3D, a Vision Transformer (ViT-Base) architecture pretrained on volumetric inputs with a spatial resolution of (12, 224, 224) and a patch size of (4, 8, 8). For inference on full-resolution CT volumes, a sliding window approach can be employed to extract features across the entire scan. Additional TAP-CT model variants, as well as the image processor, will be released in future updates.
+
+## Preprocessing
+
+While a dedicated image processor will be released in future updates, optimal feature extraction requires the following preprocessing pipeline:
+
+1. **Orientation**: Convert the volume to LPS (Left-Posterior-Superior) orientation. While the model is likely orientation-invariant, all evaluations were conducted using LPS orientation.
+2. **Spatial Resizing**: Resize the volume to a spatial resolution of \(z, 224, 224\) or \(z, 512, 512\), where \(z\) represents the number of slices along the axial dimension.
+3. **Axial Padding**: Apply zero-padding along the \(z\)-axis to ensure divisibility by 4, accommodating the model's patch size of (4, 8, 8).
+4. **Intensity Clipping**: Clip voxel intensities to the range \([-1008.0, 822.0]\) HU (Hounsfield Units).
+5. **Normalization**: Apply z-score normalization using \(mean = -86.8086\) and \(std = 322.6347\).
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModel
+
+# Load the model
+model = AutoModel.from_pretrained('nki-radiology/tap-ct-b-3d', trust_remote_code=True)
+
+# Prepare input (batch_size, channels, depth, height, width)
+x = torch.randn((16, 1, 12, 224, 224))
+
+# Forward pass
+output = model.forward(x)
+```
+
+The model returns a `BaseModelOutputWithPooling` object from the transformers library. The `output.pooler_output` contains the pooled `[CLS]` token representation, while `output.last_hidden_state` contains the spatial patch token embeddings. To extract features from all intermediate transformer layers, pass `output_hidden_states=True` to the forward method.
+
+## Model Details
+
+- **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