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project-lighter/ct_fm_feature_extractor

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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CT-FM Feature Extractor

This model is a feature extractor for CT-FM, a model pre-trained using contrastive self-supervised learning on a huge dataset of 148,000 CT scans from the Imaging Data Commons.

The backbone is based on a SegResNet, a 3D U-Net variant. If you want to just load the model and fine-tune, ignore the feature extraction workflow.

Running instructions

CT-FM Feature Extractor

This notebook demonstrates how to:

1.Load a SSL pre-trained model
2.Set up preprocessing and postprocessing pipelines
3.Perform inference on CT volumes
4.Plot distribution of features extracted

Setup

Install requirements and import necessary packages

python

# Install lighter_zoo package
%pip install lighter_zoo -U -qq

Note: you may need to restart the kernel to use updated packages.

python

# Imports
import torch
from lighter_zoo import SegResEncoder
from monai.transforms import (
    Compose, LoadImage, EnsureType, Orientation,
    ScaleIntensityRange, CropForeground
)
from monai.inferers import SlidingWindowInferer

Load Model

Download and initialize the pre-trained model from HuggingFace Hub

python

# Load pre-trained model
model = SegResEncoder.from_pretrained(
    "project-lighter/ct_fm_feature_extractor"
)
model.eval()

Setup Processing Pipelines

Define preprocessing transforms

python

# Preprocessing pipeline
preprocess = Compose([
    LoadImage(ensure_channel_first=True),  # Load image and ensure channel dimension
    EnsureType(),                         # Ensure correct data type
    Orientation(axcodes="SPL"),           # Standardize orientation
    # Scale intensity to [0,1] range, clipping outliers
    ScaleIntensityRange(
        a_min=-1024,    # Min HU value
        a_max=2048,     # Max HU value
        b_min=0,        # Target min
        b_max=1,        # Target max
        clip=True       # Clip values outside range
    ),
    CropForeground()    # Remove background to reduce computation
])

monai.transforms.croppad.array CropForeground._init:allowsmaller: Current default value of argument allow_smaller=True has been deprecated since version 1.2. It will be changed to allow_smaller=False in version 1.5.

Run Inference

Process an input CT scan and extract features

python

# Input path
input_path = "/home/suraj/Repositories/lighter-ct-fm/semantic-search-app/assets/scans/s0114.nii.gz"

# Preprocess input
input_tensor = preprocess(input_path)

# Run inference
with torch.no_grad():
    output = model(input_tensor.unsqueeze(0))[-1]

    # Average pooling compressed the feature vector across all patches. If this is not desired, remove this line and 
    # use the output tensor directly which will give you the feature maps in a low-dimensional space.
    avg_output = torch.nn.functional.adaptive_avg_pool3d(output, 1).squeeze()

print("✅ Feature extraction completed")
print(f"Output shape: {avg_output.shape}")

✅ Feature extraction completed Output shape: torch.Size([512])

python

# Plot distribution of features
import matplotlib.pyplot as plt
_ = plt.hist(avg_output.cpu().numpy(), bins=100)

[image]

python