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LICENSE

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+MIT License
+
+Original Sybil Model:
+Copyright (c) 2022 Peter Mikhael & Jeremy Wohlwend
+
+Hugging Face Adaptation:
+Copyright (c) 2024 Lab-Rasool
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+tags:
+- medical
+- cancer
+- ct-scan
+- risk-prediction
+- healthcare
+- pytorch
+- vision
+datasets:
+- NLST
+metrics:
+- auc
+- c-index
+language:
+- en
+library_name: transformers
+pipeline_tag: image-classification
+---
+
+# Sybil - Lung Cancer Risk Prediction
+
+## Model Description
+
+Sybil is a validated deep learning model that predicts future lung cancer risk from a single low-dose chest CT (LDCT) scan. Published in the Journal of Clinical Oncology, this model can assess cancer risk over a 1-6 year timeframe.
+
+### Key Features
+- **Single Scan Analysis**: Requires only one LDCT scan
+- **Multi-Year Prediction**: Provides risk scores for years 1-6
+- **Validated Performance**: Tested across multiple institutions globally
+- **Ensemble Approach**: Uses 5 models for robust predictions
+
+## Model Details
+
+- **Developed by**: MIT CSAIL & Mass General Cancer Center (Original)
+- **Adapted by**: Lab-Rasool (Hugging Face version)
+- **Model type**: 3D Convolutional Neural Network
+- **Architecture**: 3D ResNet-18 with multi-attention pooling
+- **Input**: LDCT scans (200 slices × 256×256 pixels)
+- **Output**: 6 risk scores (years 1-6)
+- **License**: MIT
+
+## Performance Metrics
+
+| Dataset | 1-Year AUC | 6-Year AUC |
+|---------|------------|------------|
+| NLST Test | 0.94 | 0.86 |
+| MGH | 0.86 | 0.75 |
+| CGMH Taiwan | 0.94 | 0.80 |
+
+## Usage
+
+```python
+from huggingface_sybil import SybilHFWrapper, SybilConfig
+
+# Load model
+config = SybilConfig()
+model = SybilHFWrapper.from_pretrained("Lab-Rasool/sybil")
+
+# Prepare DICOM files
+dicom_paths = ["scan1.dcm", "scan2.dcm", ...]
+
+# Get predictions
+output = model(dicom_paths=dicom_paths)
+risk_scores = output.risk_scores
+
+# Display results
+for year, score in enumerate(risk_scores, 1):
+    print(f"Year {year}: {score:.1%} risk")
+```
+
+## Intended Use
+
+### Primary Use Cases
+- Risk stratification in lung cancer screening programs
+- Research on lung cancer prediction models
+- Clinical decision support (with appropriate oversight)
+
+### Users
+- Healthcare providers
+- Medical researchers
+- Screening program coordinators
+
+### Out of Scope
+- Diagnosis of existing cancer
+- Use with non-LDCT imaging (X-rays, MRI)
+- Sole basis for clinical decisions
+
+## Training Data
+
+Trained on the National Lung Screening Trial (NLST) dataset:
+- ~50,000 participants
+- Ages 55-74
+- Current/former heavy smokers
+- 3 annual LDCT scans
+
+## Ethical Considerations
+
+⚠️ **Medical AI Notice**: This model should supplement, not replace, clinical judgment. Always consider:
+- Complete patient history
+- Other risk factors
+- Current screening guidelines
+- Need for human oversight
+
+## Limitations
+
+- Optimized for screening-eligible population (55-80 years)
+- Requires LDCT scans specifically
+- Performance may vary across different CT scanners
+- Not validated for non-screening populations
+
+## Citation
+
+**Original Paper:**
+```bibtex
+@article{mikhael2023sybil,
+  title={Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography},
+  author={Mikhael, Peter G and Wohlwend, Jeremy and Yala, Adam and Karstens, Ludvig and Xiang, Justin and Takigami, Angelo K and Bourgouin, Patrick P and Chan, PuiYee and Mrah, Sofiane and Amayri, Wael and others},
+  journal={Journal of Clinical Oncology},
+  volume={41},
+  number={12},
+  pages={2191--2200},
+  year={2023},
+  publisher={American Society of Clinical Oncology}
+}
+```
+
+## Acknowledgments
+
+This Hugging Face implementation is based on the original work by Peter G. Mikhael, Jeremy Wohlwend, and the team at MIT CSAIL and Massachusetts General Hospital. Original model and code available at [GitHub](https://github.com/reginabarzilaygroup/Sybil).
+
+## Model Card Contact
+
+For questions about this Hugging Face implementation: Lab-Rasool
+For questions about the original model: See the [original repository](https://github.com/reginabarzilaygroup/Sybil)

__init__.py

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+"""Hugging Face Sybil model for lung cancer risk prediction"""
+
+from .configuration_sybil import SybilConfig
+from .modeling_sybil import (
+    SybilForRiskPrediction,
+    SybilPreTrainedModel,
+    SybilOutput,
+    SybilEnsemble,
+)
+from .image_processing_sybil import SybilImageProcessor
+
+__version__ = "1.0.0"
+
+__all__ = [
+    "SybilConfig",
+    "SybilForRiskPrediction",
+    "SybilPreTrainedModel",
+    "SybilOutput",
+    "SybilEnsemble",
+    "SybilImageProcessor",
+]

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+    ]
+  },
+  "censoring_distribution": "weibull",
+  "dropout": 0.0,
+  "ensemble_size": 5,
+  "hidden_dim": 512,
+  "img_size": [
+    512,
+    512
+  ],
+  "initializer_range": 0.02,
+  "max_followup": 6,
+  "model_type": "sybil",
+  "num_images": 208,
+  "transformers_version": "4.53.2",
+  "voxel_spacing": [
+    0.703125,
+    0.703125,
+    2.5
+  ]
+}

configuration_sybil.py

@@ -0,0 +1,63 @@
+"""Sybil model configuration"""
+
+from transformers import PretrainedConfig
+from typing import Optional, List, Dict
+import json
+
+
+class SybilConfig(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`SybilForRiskPrediction`].
+    It is used to instantiate a Sybil model according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs.
+
+    Args:
+        hidden_dim (`int`, *optional*, defaults to 512):
+            Dimensionality of the hidden representations.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers.
+        max_followup (`int`, *optional*, defaults to 6):
+            Maximum number of years for risk prediction.
+        num_images (`int`, *optional*, defaults to 208):
+            Number of CT scan slices to process.
+        img_size (`List[int]`, *optional*, defaults to `[512, 512]`):
+            Size of input images after preprocessing.
+        voxel_spacing (`List[float]`, *optional*, defaults to `[0.703125, 0.703125, 2.5]`):
+            Target voxel spacing for CT scans (row, column, slice thickness).
+        censoring_distribution (`str`, *optional*, defaults to "weibull"):
+            Distribution used for censoring in survival analysis.
+        ensemble_size (`int`, *optional*, defaults to 5):
+            Number of models in the ensemble.
+        calibrator_data (`Dict`, *optional*):
+            Calibration data for risk score adjustment.
+    """
+
+    model_type = "sybil"
+
+    def __init__(
+        self,
+        hidden_dim: int = 512,
+        dropout: float = 0.0,
+        max_followup: int = 6,
+        num_images: int = 208,
+        img_size: List[int] = None,
+        voxel_spacing: List[float] = None,
+        censoring_distribution: str = "weibull",
+        ensemble_size: int = 5,
+        calibrator_data: Optional[Dict] = None,
+        initializer_range: float = 0.02,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_dim = hidden_dim
+        self.dropout = dropout
+        self.max_followup = max_followup
+        self.num_images = num_images
+        self.img_size = img_size if img_size is not None else [512, 512]
+        self.voxel_spacing = voxel_spacing if voxel_spacing is not None else [0.703125, 0.703125, 2.5]
+        self.censoring_distribution = censoring_distribution
+        self.ensemble_size = ensemble_size
+        self.calibrator_data = calibrator_data
+        self.initializer_range = initializer_range

example.py

@@ -0,0 +1,74 @@
+"""
+Simple example of using Sybil for lung cancer risk prediction
+"""
+
+import sys
+import os
+
+# Install required packages if needed
+# !pip install torch torchvision transformers pydicom torchio sybil
+
+# Import the model
+from modeling_sybil_wrapper import SybilHFWrapper
+from configuration_sybil import SybilConfig
+
+def predict_cancer_risk(dicom_paths):
+    """
+    Predict lung cancer risk from DICOM files.
+
+    Args:
+        dicom_paths: List of paths to DICOM files from a CT scan
+
+    Returns:
+        Risk scores for years 1-6
+    """
+    # Load model
+    print("Loading Sybil model...")
+    config = SybilConfig()
+    model = SybilHFWrapper.from_pretrained("Lab-Rasool/sybil")
+
+    # Run prediction
+    print(f"Processing {len(dicom_paths)} DICOM files...")
+    output = model(dicom_paths=dicom_paths, return_attentions=False)
+
+    # Extract risk scores
+    risk_scores = output.risk_scores.numpy()
+
+    return risk_scores
+
+
+def main():
+    # Example usage with demo data
+    # In practice, replace with actual DICOM file paths
+    demo_dicom_paths = [
+        "path/to/slice001.dcm",
+        "path/to/slice002.dcm",
+        # ... more slices
+    ]
+
+    # For testing, you can download demo data:
+    # Download from: https://github.com/reginabarzilaygroup/Sybil
+
+    print("=" * 50)
+    print("Sybil Lung Cancer Risk Prediction")
+    print("=" * 50)
+
+    # Uncomment when you have actual DICOM files:
+    # risk_scores = predict_cancer_risk(demo_dicom_paths)
+
+    # Print results
+    # print("\nLung Cancer Risk Predictions:")
+    # print("-" * 30)
+    # for year, score in enumerate(risk_scores, 1):
+    #     risk_pct = score * 100
+    #     print(f"Year {year}: {risk_pct:.2f}% risk")
+
+    print("\nNote: This example requires actual DICOM files.")
+    print("Please provide paths to LDCT scan DICOM files.")
+    print("\nFor more information:")
+    print("- Original paper: https://doi.org/10.1200/JCO.22.01345")
+    print("- GitHub: https://github.com/reginabarzilaygroup/Sybil")
+
+
+if __name__ == "__main__":
+    main()

image_processing_sybil.py

@@ -0,0 +1,315 @@
+"""Image processor for Sybil CT scan preprocessing"""
+
+import numpy as np
+import torch
+from typing import Dict, List, Optional, Union, Tuple
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from transformers.utils import TensorType
+import pydicom
+from PIL import Image
+import torchio as tio
+
+
+def order_slices(dicoms: List) -> List:
+    """Order DICOM slices by their position"""
+    # Sort by ImagePositionPatient if available
+    try:
+        dicoms = sorted(dicoms, key=lambda x: float(x.ImagePositionPatient[2]))
+    except (AttributeError, TypeError):
+        # Fall back to InstanceNumber if ImagePositionPatient not available
+        try:
+            dicoms = sorted(dicoms, key=lambda x: int(x.InstanceNumber))
+        except (AttributeError, TypeError):
+            pass  # Keep original order if neither attribute is available
+    return dicoms
+
+
+class SybilImageProcessor(BaseImageProcessor):
+    """
+    Constructs a Sybil image processor for preprocessing CT scans.
+
+    Args:
+        voxel_spacing (`List[float]`, *optional*, defaults to `[0.703125, 0.703125, 2.5]`):
+            Target voxel spacing for resampling (row, column, slice thickness).
+        img_size (`List[int]`, *optional*, defaults to `[512, 512]`):
+            Target image size after resizing.
+        num_images (`int`, *optional*, defaults to `208`):
+            Number of slices to use from the CT scan.
+        windowing (`Dict[str, float]`, *optional*):
+            Windowing parameters for CT scan visualization.
+            Default uses lung window: center=-600, width=1500.
+        normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize pixel values to [0, 1].
+        **kwargs:
+            Additional keyword arguments passed to the parent class.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        voxel_spacing: List[float] = None,
+        img_size: List[int] = None,
+        num_images: int = 208,
+        windowing: Dict[str, float] = None,
+        normalize: bool = True,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        self.voxel_spacing = voxel_spacing if voxel_spacing is not None else [0.703125, 0.703125, 2.5]
+        self.img_size = img_size if img_size is not None else [512, 512]
+        self.num_images = num_images
+
+        # Default lung window settings
+        self.windowing = windowing if windowing is not None else {
+            "center": -600,
+            "width": 1500
+        }
+        self.normalize = normalize
+
+        # TorchIO transforms for standardization
+        self.resample_transform = tio.transforms.Resample(target=self.voxel_spacing)
+        # Note: Original Sybil uses 200 depth, 256x256 images
+        self.default_depth = 200
+        self.default_size = [256, 256]
+        self.padding_transform = tio.transforms.CropOrPad(
+            target_shape=(self.default_depth, *self.default_size),
+            padding_mode=0
+        )
+
+    def load_dicom_series(self, paths: List[str]) -> Tuple[np.ndarray, Dict]:
+        """
+        Load a series of DICOM files.
+
+        Args:
+            paths: List of paths to DICOM files.
+
+        Returns:
+            Tuple of (volume array, metadata dict)
+        """
+        dicoms = []
+        for path in paths:
+            try:
+                dcm = pydicom.dcmread(path, stop_before_pixels=False)
+                dicoms.append(dcm)
+            except Exception as e:
+                print(f"Error reading DICOM file {path}: {e}")
+                continue
+
+        if not dicoms:
+            raise ValueError("No valid DICOM files found")
+
+        # Order slices by position
+        dicoms = order_slices(dicoms)
+
+        # Extract pixel arrays
+        volume = np.stack([dcm.pixel_array.astype(np.float32) for dcm in dicoms])
+
+        # Extract metadata
+        metadata = {
+            "slice_thickness": float(dicoms[0].SliceThickness) if hasattr(dicoms[0], 'SliceThickness') else None,
+            "pixel_spacing": list(map(float, dicoms[0].PixelSpacing)) if hasattr(dicoms[0], 'PixelSpacing') else None,
+            "manufacturer": str(dicoms[0].Manufacturer) if hasattr(dicoms[0], 'Manufacturer') else None,
+            "num_slices": len(dicoms)
+        }
+
+        # Apply rescale if present
+        if hasattr(dicoms[0], 'RescaleSlope') and hasattr(dicoms[0], 'RescaleIntercept'):
+            slope = float(dicoms[0].RescaleSlope)
+            intercept = float(dicoms[0].RescaleIntercept)
+            volume = volume * slope + intercept
+
+        return volume, metadata
+
+    def load_png_series(self, paths: List[str]) -> np.ndarray:
+        """
+        Load a series of PNG files.
+
+        Args:
+            paths: List of paths to PNG files (must be in anatomical order).
+
+        Returns:
+            3D volume array
+        """
+        images = []
+        for path in paths:
+            img = Image.open(path).convert('L')  # Convert to grayscale
+            images.append(np.array(img, dtype=np.float32))
+
+        return np.stack(images)
+
+    def apply_windowing(self, volume: np.ndarray) -> np.ndarray:
+        """
+        Apply windowing to CT scan for better visualization.
+
+        Args:
+            volume: 3D CT volume.
+
+        Returns:
+            Windowed volume.
+        """
+        center = self.windowing["center"]
+        width = self.windowing["width"]
+
+        # Calculate window boundaries
+        lower = center - width / 2
+        upper = center + width / 2
+
+        # Apply windowing
+        volume = np.clip(volume, lower, upper)
+
+        # Normalize to [0, 1] if requested
+        if self.normalize:
+            volume = (volume - lower) / (upper - lower)
+
+        return volume
+
+    def resample_volume(
+        self,
+        volume: torch.Tensor,
+        original_spacing: Optional[List[float]] = None
+    ) -> torch.Tensor:
+        """
+        Resample volume to target voxel spacing.
+
+        Args:
+            volume: 3D volume tensor.
+            original_spacing: Original voxel spacing.
+
+        Returns:
+            Resampled volume.
+        """
+        # Create TorchIO subject
+        subject = tio.Subject(
+            image=tio.ScalarImage(tensor=volume.unsqueeze(0), spacing=original_spacing)
+        )
+
+        # Apply resampling
+        resampled = self.resample_transform(subject)
+
+        return resampled['image'].data.squeeze(0)
+
+    def pad_or_crop_volume(self, volume: torch.Tensor) -> torch.Tensor:
+        """
+        Pad or crop volume to target shape.
+
+        Args:
+            volume: 3D volume tensor.
+
+        Returns:
+            Padded/cropped volume.
+        """
+        # Create TorchIO subject
+        subject = tio.Subject(
+            image=tio.ScalarImage(tensor=volume.unsqueeze(0))
+        )
+
+        # Apply padding/cropping
+        transformed = self.padding_transform(subject)
+
+        return transformed['image'].data.squeeze(0)
+
+    def preprocess(
+        self,
+        images: Union[List[str], np.ndarray, torch.Tensor],
+        file_type: str = "dicom",
+        voxel_spacing: Optional[List[float]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **kwargs
+    ) -> BatchFeature:
+        """
+        Preprocess CT scan images.
+
+        Args:
+            images: Either list of file paths or numpy/torch array of images.
+            file_type: Type of input files ("dicom" or "png").
+            voxel_spacing: Original voxel spacing (required for PNG files).
+            return_tensors: The type of tensors to return.
+
+        Returns:
+            BatchFeature with preprocessed images.
+        """
+        # Load images if paths are provided
+        if isinstance(images, list) and isinstance(images[0], str):
+            if file_type == "dicom":
+                volume, metadata = self.load_dicom_series(images)
+                if voxel_spacing is None and metadata["pixel_spacing"]:
+                    voxel_spacing = metadata["pixel_spacing"] + [metadata["slice_thickness"]]
+            elif file_type == "png":
+                if voxel_spacing is None:
+                    raise ValueError("voxel_spacing must be provided for PNG files")
+                volume = self.load_png_series(images)
+            else:
+                raise ValueError(f"Unknown file type: {file_type}")
+        elif isinstance(images, (np.ndarray, torch.Tensor)):
+            volume = images
+        else:
+            raise ValueError("Images must be file paths, numpy array, or torch tensor")
+
+        # Convert to torch tensor
+        if isinstance(volume, np.ndarray):
+            volume = torch.from_numpy(volume).float()
+
+        # Apply windowing
+        if isinstance(volume, torch.Tensor):
+            volume_np = volume.numpy()
+        else:
+            volume_np = volume
+        volume_np = self.apply_windowing(volume_np)
+        volume = torch.from_numpy(volume_np).float()
+
+        # Resample if spacing is provided
+        if voxel_spacing is not None:
+            volume = self.resample_volume(volume, voxel_spacing)
+
+        # Pad or crop to target shape
+        volume = self.pad_or_crop_volume(volume)
+
+        # Reshape to match original Sybil format: (D, H, W) -> (C, D, H, W)
+        # The model expects 3 channels (RGB format), so repeat grayscale to 3 channels
+        volume = volume.unsqueeze(0).repeat(3, 1, 1, 1)  # Now (3, D, H, W)
+
+        # Prepare output
+        data = {"pixel_values": volume}
+
+        # Convert to requested tensor type
+        if return_tensors == "pt":
+            return BatchFeature(data=data, tensor_type=TensorType.PYTORCH)
+        elif return_tensors == "np":
+            data = {k: v.numpy() for k, v in data.items()}
+            return BatchFeature(data=data, tensor_type=TensorType.NUMPY)
+        else:
+            return BatchFeature(data=data)
+
+    def __call__(
+        self,
+        images: Union[List[str], List[List[str]], np.ndarray, torch.Tensor],
+        **kwargs
+    ) -> BatchFeature:
+        """
+        Main method to prepare images for the model.
+
+        Args:
+            images: Images to preprocess. Can be:
+                - List of file paths for a single series
+                - List of lists of file paths for multiple series
+                - Numpy array or torch tensor
+
+        Returns:
+            BatchFeature with preprocessed images ready for model input.
+        """
+        # Handle batch processing
+        if isinstance(images, list) and images and isinstance(images[0], list):
+            # Multiple series
+            batch_volumes = []
+            for series_paths in images:
+                result = self.preprocess(series_paths, **kwargs)
+                batch_volumes.append(result["pixel_values"])
+
+            # Stack into batch (B, C, D, H, W)
+            pixel_values = torch.stack(batch_volumes)
+            return BatchFeature(data={"pixel_values": pixel_values})
+        else:
+            # Single series
+            return self.preprocess(images, **kwargs)

modeling_sybil.py

@@ -0,0 +1,385 @@
+"""PyTorch Sybil model for lung cancer risk prediction"""
+
+import torch
+import torch.nn as nn
+import torchvision
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import BaseModelOutput
+from typing import Optional, Dict, List, Tuple
+import numpy as np
+from dataclasses import dataclass
+
+try:
+    from .configuration_sybil import SybilConfig
+except ImportError:
+    from configuration_sybil import SybilConfig
+
+
+@dataclass
+class SybilOutput(BaseModelOutput):
+    """
+    Base class for Sybil model outputs.
+
+    Args:
+        risk_scores: (`torch.FloatTensor` of shape `(batch_size, max_followup)`):
+            Predicted risk scores for each year up to max_followup.
+        image_attention: (`torch.FloatTensor` of shape `(batch_size, num_slices, height, width)`, *optional*):
+            Attention weights over image pixels.
+        volume_attention: (`torch.FloatTensor` of shape `(batch_size, num_slices)`, *optional*):
+            Attention weights over CT scan slices.
+        hidden_states: (`torch.FloatTensor` of shape `(batch_size, hidden_dim)`, *optional*):
+            Hidden states from the pooling layer.
+    """
+    risk_scores: torch.FloatTensor = None
+    image_attention: Optional[torch.FloatTensor] = None
+    volume_attention: Optional[torch.FloatTensor] = None
+    hidden_states: Optional[torch.FloatTensor] = None
+
+
+class CumulativeProbabilityLayer(nn.Module):
+    """Cumulative probability layer for survival prediction"""
+
+    def __init__(self, hidden_dim: int, max_followup: int = 6):
+        super().__init__()
+        self.max_followup = max_followup
+        self.fc = nn.Linear(hidden_dim, max_followup)
+
+    def forward(self, x):
+        logits = self.fc(x)
+        # Apply cumulative sum for monotonic risk scores
+        cumsum = torch.cumsum(torch.sigmoid(logits), dim=-1)
+        # Normalize to [0, 1] range
+        return cumsum / self.max_followup
+
+
+class MultiAttentionPool(nn.Module):
+    """Multi-attention pooling layer for CT scan aggregation"""
+
+    def __init__(self, channels: int = 512):
+        super().__init__()
+        self.channels = channels
+
+        # Volume-level attention (across slices)
+        self.volume_attention = nn.Sequential(
+            nn.Conv3d(channels, 128, kernel_size=1),
+            nn.ReLU(),
+            nn.Conv3d(128, 1, kernel_size=1)
+        )
+
+        # Image-level attention (within slices)
+        self.image_attention = nn.Sequential(
+            nn.Conv3d(channels, 128, kernel_size=1),
+            nn.ReLU(),
+            nn.Conv3d(128, 1, kernel_size=1)
+        )
+
+    def forward(self, x):
+        batch_size = x.shape[0]
+
+        # Compute attention weights
+        volume_att = self.volume_attention(x)  # [B, 1, D, H, W]
+        image_att = self.image_attention(x)    # [B, 1, D, H, W]
+
+        # Apply softmax for normalization
+        volume_att_flat = volume_att.view(batch_size, -1)
+        volume_att_weights = torch.softmax(volume_att_flat, dim=-1)
+        volume_att_weights = volume_att_weights.view_as(volume_att)
+
+        image_att_2d = image_att.squeeze(1)  # [B, D, H, W]
+        for i in range(image_att_2d.shape[1]):  # For each slice
+            slice_att = image_att_2d[:, i, :, :].contiguous()
+            slice_att_flat = slice_att.view(batch_size, -1)
+            slice_att_weights = torch.softmax(slice_att_flat, dim=-1)
+            image_att_2d[:, i, :, :] = slice_att_weights.view_as(slice_att)
+        image_att = image_att_2d.unsqueeze(1)
+
+        # Apply attention and pool
+        attended = x * volume_att_weights * image_att
+        hidden = attended.mean(dim=[2, 3, 4])  # Global average pooling
+
+        return {
+            'hidden': hidden,
+            'volume_attention_1': volume_att_weights.squeeze(1),
+            'image_attention_1': image_att.squeeze(1)
+        }
+
+
+class SybilPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface
+    for downloading and loading pretrained models.
+    """
+    config_class = SybilConfig
+    base_model_prefix = "sybil"
+    supports_gradient_checkpointing = False
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Conv3d):
+            nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
+            if module.bias is not None:
+                module.bias.data.zero_()
+
+
+class SybilForRiskPrediction(SybilPreTrainedModel):
+    """
+    Sybil model for lung cancer risk prediction from CT scans.
+
+    This model takes 3D CT scan volumes as input and predicts cancer risk scores
+    for multiple future time points (typically 1-6 years).
+    """
+
+    def __init__(self, config: SybilConfig):
+        super().__init__(config)
+        self.config = config
+
+        # Use pretrained R3D-18 as backbone
+        encoder = torchvision.models.video.r3d_18(pretrained=True)
+        self.image_encoder = nn.Sequential(*list(encoder.children())[:-2])
+
+        # Multi-attention pooling
+        self.pool = MultiAttentionPool(channels=512)
+
+        # Classification layers
+        self.relu = nn.ReLU(inplace=False)
+        self.dropout = nn.Dropout(p=config.dropout)
+
+        # Risk prediction layer
+        self.prob_of_failure_layer = CumulativeProbabilityLayer(
+            config.hidden_dim,
+            max_followup=config.max_followup
+        )
+
+        # Calibrator for ensemble predictions
+        self.calibrator = None
+        if config.calibrator_data:
+            self.set_calibrator(config.calibrator_data)
+
+        # Initialize weights
+        self.post_init()
+
+    def set_calibrator(self, calibrator_data: Dict):
+        """Set calibration data for risk score adjustment"""
+        self.calibrator = calibrator_data
+
+    def _calibrate_scores(self, scores: torch.Tensor) -> torch.Tensor:
+        """Apply calibration to raw risk scores"""
+        if self.calibrator is None:
+            return scores
+
+        # Convert to numpy for calibration
+        scores_np = scores.detach().cpu().numpy()
+        calibrated = np.zeros_like(scores_np)
+
+        # Apply calibration for each year
+        for year in range(scores_np.shape[1]):
+            year_key = f"Year{year + 1}"
+            if year_key in self.calibrator:
+                # Apply calibration transformation
+                calibrated[:, year] = self._apply_calibration(
+                    scores_np[:, year],
+                    self.calibrator[year_key]
+                )
+            else:
+                calibrated[:, year] = scores_np[:, year]
+
+        return torch.from_numpy(calibrated).to(scores.device)
+
+    def _apply_calibration(self, scores: np.ndarray, calibrator_params: Dict) -> np.ndarray:
+        """Apply specific calibration transformation"""
+        # Simplified calibration - in practice, this would use the full calibration model
+        # from the original Sybil implementation
+        return scores  # Placeholder for now
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor,
+        return_attentions: bool = False,
+        return_dict: bool = True,
+    ) -> SybilOutput:
+        """
+        Forward pass of the Sybil model.
+
+        Args:
+            pixel_values: (`torch.FloatTensor` of shape `(batch_size, channels, depth, height, width)`):
+                Pixel values of CT scan volumes.
+            return_attentions: (`bool`, *optional*, defaults to `False`):
+                Whether to return attention weights.
+            return_dict: (`bool`, *optional*, defaults to `True`):
+                Whether to return a `SybilOutput` instead of a plain tuple.
+
+        Returns:
+            `SybilOutput` or tuple
+        """
+        # Extract features using 3D CNN backbone
+        features = self.image_encoder(pixel_values)
+
+        # Apply multi-attention pooling
+        pool_output = self.pool(features)
+
+        # Apply ReLU and dropout
+        hidden = self.relu(pool_output['hidden'])
+        hidden = self.dropout(hidden)
+
+        # Predict risk scores
+        risk_logits = self.prob_of_failure_layer(hidden)
+        risk_scores = torch.sigmoid(risk_logits)
+
+        # Apply calibration if available
+        risk_scores = self._calibrate_scores(risk_scores)
+
+        if not return_dict:
+            outputs = (risk_scores,)
+            if return_attentions:
+                outputs = outputs + (pool_output.get('image_attention_1'),
+                                   pool_output.get('volume_attention_1'))
+            return outputs
+
+        return SybilOutput(
+            risk_scores=risk_scores,
+            image_attention=pool_output.get('image_attention_1') if return_attentions else None,
+            volume_attention=pool_output.get('volume_attention_1') if return_attentions else None,
+            hidden_states=hidden if return_attentions else None
+        )
+
+    @classmethod
+    def from_pretrained_ensemble(
+        cls,
+        pretrained_model_name_or_path,
+        checkpoint_paths: List[str],
+        calibrator_path: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        Load an ensemble of Sybil models from checkpoints.
+
+        Args:
+            pretrained_model_name_or_path: Path to the pretrained model or model identifier.
+            checkpoint_paths: List of paths to individual model checkpoints.
+            calibrator_path: Path to calibration data.
+            **kwargs: Additional keyword arguments for model initialization.
+
+        Returns:
+            SybilEnsemble: An ensemble of Sybil models.
+        """
+        config = kwargs.pop("config", None)
+        if config is None:
+            config = SybilConfig.from_pretrained(pretrained_model_name_or_path)
+
+        # Load calibrator if provided
+        calibrator_data = None
+        if calibrator_path:
+            import json
+            with open(calibrator_path, 'r') as f:
+                calibrator_data = json.load(f)
+            config.calibrator_data = calibrator_data
+
+        # Create ensemble
+        models = []
+        for checkpoint_path in checkpoint_paths:
+            model = cls(config)
+            # Load checkpoint weights
+            checkpoint = torch.load(checkpoint_path, map_location='cpu')
+            # Remove 'model.' prefix from state dict keys if present
+            state_dict = {}
+            for k, v in checkpoint['state_dict'].items():
+                if k.startswith('model.'):
+                    state_dict[k[6:]] = v
+                else:
+                    state_dict[k] = v
+
+            # Map to new model structure
+            mapped_state_dict = model._map_checkpoint_weights(state_dict)
+            model.load_state_dict(mapped_state_dict, strict=False)
+            models.append(model)
+
+        return SybilEnsemble(models, config)
+
+    def _map_checkpoint_weights(self, state_dict: Dict) -> Dict:
+        """Map original Sybil checkpoint weights to new structure"""
+        mapped = {}
+
+        # Map encoder weights
+        for k, v in state_dict.items():
+            if k.startswith('image_encoder'):
+                mapped[k] = v
+            elif k.startswith('pool'):
+                # Map pooling layer weights
+                mapped[k] = v
+            elif k.startswith('prob_of_failure_layer'):
+                # Map final prediction layer
+                mapped[k] = v
+
+        return mapped
+
+
+class SybilEnsemble:
+    """Ensemble of Sybil models for improved predictions"""
+
+    def __init__(self, models: List[SybilForRiskPrediction], config: SybilConfig):
+        self.models = models
+        self.config = config
+        self.device = None
+
+    def to(self, device):
+        """Move all models to device"""
+        self.device = device
+        for model in self.models:
+            model.to(device)
+        return self
+
+    def eval(self):
+        """Set all models to evaluation mode"""
+        for model in self.models:
+            model.eval()
+
+    def __call__(
+        self,
+        pixel_values: torch.FloatTensor,
+        return_attentions: bool = False,
+    ) -> SybilOutput:
+        """
+        Run inference with ensemble voting.
+
+        Args:
+            pixel_values: Input CT scan volumes.
+            return_attentions: Whether to return attention maps.
+
+        Returns:
+            SybilOutput with averaged predictions from all models.
+        """
+        all_risk_scores = []
+        all_image_attentions = []
+        all_volume_attentions = []
+
+        with torch.no_grad():
+            for model in self.models:
+                output = model(
+                    pixel_values=pixel_values,
+                    return_attentions=return_attentions
+                )
+                all_risk_scores.append(output.risk_scores)
+
+                if return_attentions:
+                    all_image_attentions.append(output.image_attention)
+                    all_volume_attentions.append(output.volume_attention)
+
+        # Average predictions
+        risk_scores = torch.stack(all_risk_scores).mean(dim=0)
+
+        # Average attentions if requested
+        image_attention = None
+        volume_attention = None
+        if return_attentions:
+            image_attention = torch.stack(all_image_attentions).mean(dim=0)
+            volume_attention = torch.stack(all_volume_attentions).mean(dim=0)
+
+        return SybilOutput(
+            risk_scores=risk_scores,
+            image_attention=image_attention,
+            volume_attention=volume_attention
+        )

modeling_sybil_wrapper.py

@@ -0,0 +1,140 @@
+"""
+Simplified Hugging Face wrapper for original Sybil model
+This ensures full compatibility with the original implementation
+"""
+
+import os
+import sys
+import json
+import torch
+import torch.nn as nn
+from typing import Optional, List, Dict
+from transformers import PreTrainedModel
+from dataclasses import dataclass
+from transformers.modeling_outputs import BaseModelOutput
+
+# Add original Sybil to path
+sys.path.append('/mnt/f/Projects/hfsybil/Sybil')
+from sybil import Sybil as OriginalSybil
+from sybil import Serie
+
+try:
+    from .configuration_sybil import SybilConfig
+except ImportError:
+    from configuration_sybil import SybilConfig
+
+
+@dataclass
+class SybilOutput(BaseModelOutput):
+    """
+    Output class for Sybil model.
+    """
+    risk_scores: torch.FloatTensor = None
+    attentions: Optional[Dict] = None
+
+
+class SybilHFWrapper(PreTrainedModel):
+    """
+    Hugging Face wrapper around the original Sybil model.
+    This ensures complete compatibility while providing HF interface.
+    """
+    config_class = SybilConfig
+    base_model_prefix = "sybil"
+
+    def __init__(self, config: SybilConfig):
+        super().__init__(config)
+        self.config = config
+
+        # Load the original Sybil model with ensemble
+        checkpoint_dir = "/mnt/f/Projects/hfsybil/checkpoints"
+
+        # Copy checkpoints to ~/.sybil if needed
+        cache_dir = os.path.expanduser("~/.sybil")
+        os.makedirs(cache_dir, exist_ok=True)
+
+        # Map of checkpoint files
+        checkpoint_files = {
+            "28a7cd44f5bcd3e6cc760b65c7e0d54d.ckpt": "sybil_1",
+            "56ce1a7d241dc342982f5466c4a9d7ef.ckpt": "sybil_2",
+            "624407ef8e3a2a009f9fa51f9846fe9a.ckpt": "sybil_3",
+            "64a91b25f84141d32852e75a3aec7305.ckpt": "sybil_4",
+            "65fd1f04cb4c5847d86a9ed8ba31ac1a.ckpt": "sybil_5",
+            "sybil_ensemble_simple_calibrator.json": "ensemble_calibrator"
+        }
+
+        # Copy checkpoint files
+        for filename in checkpoint_files.keys():
+            src = os.path.join(checkpoint_dir, filename)
+            dst = os.path.join(cache_dir, filename)
+            if os.path.exists(src) and not os.path.exists(dst):
+                import shutil
+                shutil.copy2(src, dst)
+
+        # Initialize the original model
+        self.sybil_model = OriginalSybil("sybil_ensemble")
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor = None,
+        dicom_paths: List[str] = None,
+        return_attentions: bool = False,
+        **kwargs
+    ) -> SybilOutput:
+        """
+        Forward pass using original Sybil model.
+
+        Args:
+            pixel_values: Pre-processed tensor (not used directly, for compatibility)
+            dicom_paths: List of DICOM file paths
+            return_attentions: Whether to return attention maps
+
+        Returns:
+            SybilOutput with risk scores and optional attentions
+        """
+
+        if dicom_paths is None:
+            raise ValueError("dicom_paths must be provided")
+
+        # Create Serie object
+        serie = Serie(dicom_paths)
+
+        # Run prediction
+        prediction = self.sybil_model.predict([serie], return_attentions=return_attentions)
+
+        # Convert to torch tensors
+        risk_scores = torch.tensor(prediction.scores[0])
+
+        return SybilOutput(
+            risk_scores=risk_scores,
+            attentions=prediction.attentions[0] if return_attentions else None
+        )
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        """
+        Load the model. Since we're using the original Sybil,
+        we just need to ensure the checkpoints are available.
+        """
+        config = kwargs.pop("config", None)
+        if config is None:
+            config = SybilConfig.from_pretrained(pretrained_model_name_or_path)
+
+        return cls(config)
+
+    def save_pretrained(self, save_directory, **kwargs):
+        """
+        Save the model configuration.
+        The actual model weights are handled by the original Sybil.
+        """
+        os.makedirs(save_directory, exist_ok=True)
+        self.config.save_pretrained(save_directory)
+
+        # Save info about checkpoint locations
+        info = {
+            "model_type": "sybil_wrapper",
+            "checkpoint_dir": "/mnt/f/Projects/hfsybil/checkpoints",
+            "note": "This model uses the original Sybil implementation"
+        }
+
+        with open(os.path.join(save_directory, "model_info.json"), "w") as f:
+            json.dump(info, f, indent=2)

requirements.txt

@@ -0,0 +1,9 @@
+torch>=2.0.0
+torchvision>=0.15.0
+transformers>=4.30.0
+pydicom>=2.3.0
+torchio>=0.19.0
+SimpleITK>=2.2.0
+numpy>=1.21.0
+Pillow>=9.0.0
+sybil>=1.2.0

sybil_1/config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "SybilForRiskPrediction"
+  ],
+  "calibrator_data": null,
+  "censoring_distribution": {
+    "0": 0.9851933387778276,
+    "1": 0.9748326267838882,
+    "2": 0.9659936099043455,
+    "3": 0.9587266943913851,
+    "4": 0.952360797355704,
+    "5": 0.9461857341570268
+  },
+  "dropout": 0.1,
+  "ensemble_size": 5,
+  "hidden_dim": 512,
+  "img_size": [
+    256,
+    256
+  ],
+  "initializer_range": 0.02,
+  "max_followup": 6,
+  "model_type": "sybil",
+  "num_images": 200,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "voxel_spacing": [
+    0.703125,
+    0.703125,
+    2.5
+  ]
+}

sybil_1/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3409876228509cb5cb993216d840253b48ffc803acf5066146276c38df03e85
+size 133255624

sybil_2/config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "SybilForRiskPrediction"
+  ],
+  "calibrator_data": null,
+  "censoring_distribution": {
+    "0": 0.9851928130104384,
+    "1": 0.9748317321074379,
+    "2": 0.9659923988537461,
+    "3": 0.9587252204657827,
+    "4": 0.9523590830936266,
+    "5": 0.9461840310101451
+  },
+  "dropout": 0.1,
+  "ensemble_size": 5,
+  "hidden_dim": 512,
+  "img_size": [
+    256,
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sybil_2/model.safetensors

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sybil_3/config.json

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sybil_3/model.safetensors

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sybil_4/config.json

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sybil_5/config.json

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