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CT-FM: A 3D Image-Based Foundation Model for Computed Tomography

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[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97-Models-yellow)](https://huggingface.co/project-lighter)
[![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2501.09001)

# CT-FM: A 3D Image-Based Foundation Model for Radiological Tasks

![CT-FM Overview](https://raw.githubusercontent.com/project-lighter/CT-FM/main/docs/assets/Figure1.png)

## Introduction
This repository contains the code and resources for CT-FM, a 3D image-based pre-trained foundation model designed for various radiological tasks. CT-FM is trained using self-supervised learning (SSL) on a large dataset of 148,000 CT scans. This model aims to address a range of tasks, including whole-body segmentation, tumor segmentation, head CT triage, and medical image retrieval. This work builds upon previous efforts in radiological AI, shifting from task-specific expert models to unified foundation models for broader adaptability and efficiency.

## Key Innovations

*   **Large-Scale 3D Pretraining:** Emphasis on 3D data rather than traditional 2D datasets.
*   **Task-Agnostic Training:** Enabling transferability across various radiological tasks.
*   **Open Source:** Model weights, data, and code are shared for collaborative development.

## Results

CT-FM has been evaluated on several downstream tasks, demonstrating strong performance:

1.  **Whole-Body Segmentation**
    *   Evaluated on the TotalSegmentator dataset (117 anatomical labels across 1,228 scans).
    *   Achieved a mean Dice coefficient of 0.898, outperforming baseline and SuPREM models on most anatomical regions. Note that nnUnet showed better results due to multi-fold cross-validation and ensemble strategies, which CT-FM didn’t employ.

2.  **Cancer Tumor Segmentation**
    *   Benchmarked on the Medical Segmentation Decathlon dataset for lung, hepatic, and pancreatic tumors.
    *   Demonstrated improved Dice scores and Average Surface Distance (ASD) in segmentation tasks for lung and hepatic tumors. For pancreatic tumors, ASD improvements were noted despite comparable Dice scores to baselines.

3.  **Head CT Triage**
    *   Evaluated on SinoCT (9,000 scans) and CQ500 (491 scans) datasets for normal/abnormal classification.
    *   Achieved an F1 score of 0.776 on SinoCT and 0.754 on CQ500, surpassing random baselines but slightly underperforming the SuPREM model in some metrics.

4.  **Medical Image Retrieval**
    *   Tested on OrganMNIST3D and 3D-MIR datasets.
    *   Outperformed baselines in retrieving scans with similar anatomical regions and lesion characteristics. Achieved top precision scores in lesion-based retrieval tasks.

5.  **Anatomical Clustering and Semantic Search**
    *   Showed inherent clustering of anatomical features in embedding space.
    *   Facilitated fine-grained semantic searches, linking specific anatomical regions across scans.

6.  **Stability and Robustness**
    *   Demonstrated consistent performance across test-retest datasets, showcasing robustness to variations in acquisition parameters.

## Methods

### Pretraining Strategy

CT-FM is pre-trained using a modified SimCLR framework for self-supervised learning:

*   **Intra-sample Contrastive Learning:** Focuses on patches within the same sample to learn spatial semantics.
*   **Augmentation Strategies:** Utilizes augmentations like random cropping, histogram shifting, and intensity scaling.
*   **Pretraining Details:** Pretrained for 500 epochs on 148,000 CT scans, selecting the best checkpoint at epoch 449.
*  **Architecture Details:** Uses a convolutional vision encoder, SegResEncoder with 77M params

### Fine-tuning for Downstream Tasks

1.  **Segmentation:**
    *   Plugs the pretrained SegResEncoder into SegResNetDS and trains with Dice score and cross-entropy loss.
    *   Trained for 300 epochs with augmentations like affine transformations and Gaussian noise.
    *   Works both with project-lighter pipelines as well as established frameworks like Auto3DSeg

2.  **Classification:**
    *   Implemented using SegResEncoder as the backbone + MLP, optimized with Binary Cross-Entropy loss.
    *   Preprocessing includes windowing levels specific to CT scan features (blood, subdural, stroke, bone).

3.  **Retrieval:**
    *   Embeddings generated for training data were compared using cosine similarity for retrieval tasks.

CT-FM

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