ATOMMIC can be extended to support other medical imaging modalities by incorporating specific data loaders, pre-processing transforms, and task-specific models tailored to the characteristics of CT or ultrasound imaging. For CT imaging, the data loaders can be designed to handle the unique data formats and structures of CT scans, while the pre-processing transforms can include CT-specific noise reduction techniques or contrast enhancement methods. Task-specific models for CT reconstruction or segmentation can be integrated into ATOMMIC to cater to the specific requirements of CT image analysis.
Similarly, for ultrasound imaging, the data loaders can be customized to handle ultrasound image data, which is typically in a different format compared to MRI data. Pre-processing transforms can include speckle reduction techniques or image enhancement methods specific to ultrasound imaging. Task-specific models for ultrasound image analysis, such as lesion detection or tissue characterization, can be included in ATOMMIC to enable comprehensive analysis of ultrasound images.
To enable cross-modality transfer learning, ATOMMIC can incorporate transfer learning techniques that leverage knowledge from one modality (e.g., MRI) to improve performance in another modality (e.g., CT or ultrasound). By fine-tuning pre-trained models on one modality with data from another modality, ATOMMIC can facilitate the transfer of learned features and representations across different imaging modalities, enhancing the generalization and performance of AI models in multi-modal medical imaging tasks.