The proposed approach of leveraging language as a high-quality supervision for medical imaging interpretation can be extended to other medical imaging modalities beyond chest CT and X-ray, such as MRI or PET scans, by adapting the model architecture and training process to accommodate the unique characteristics of these modalities.
For MRI scans, which provide detailed images of soft tissues and organs, the language-guided retrieval strategy can be modified to focus on specific anatomical structures and pathologies commonly seen in MRI reports. The model can be trained on paired MRI images and reports to learn the semantic relationships between the two modalities. Additionally, incorporating anatomical priors and physical information, similar to what was done for chest CT images, can help improve the model's understanding of MRI scans.
For PET scans, which provide functional information about tissues and organs, the language-guided retrieval strategy can be adapted to capture metabolic activity and abnormalities detected in PET reports. The model can be trained on paired PET images and reports to learn the associations between metabolic patterns and corresponding findings. Incorporating domain-specific knowledge about radiotracer uptake and distribution can enhance the model's ability to interpret PET scans accurately.
By customizing the training data, model architecture, and feature extraction methods for each modality, the proposed approach can be extended to a wide range of medical imaging modalities, enabling automated interpretation and diagnosis across different imaging techniques.