PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation
This work addresses a practical problem in medical imaging for cancer detection by enabling flexible and efficient adaptation of segmentation models across modalities, though it is incremental in leveraging existing transformer and LoRA techniques.
The paper tackles the challenge of limited PET scan availability in multi-modal medical image segmentation by proposing a parameter-efficient adaptation framework that allows a model trained only on CT scans to incorporate PET scans when available, achieving comparable performance to early fusion methods with only 8% of trainable parameters and a +28% improvement in average dice score on PET scans.
Imaging modalities such as Computed Tomography (CT) and Positron Emission Tomography (PET) are key in cancer detection, inspiring Deep Neural Networks (DNN) models that merge these scans for tumor segmentation. When both CT and PET scans are available, it is common to combine them as two channels of the input to the segmentation model. However, this method requires both scan types during training and inference, posing a challenge due to the limited availability of PET scans, thereby sometimes limiting the process to CT scans only. Hence, there is a need to develop a flexible DNN architecture that can be trained/updated using only CT scans but can effectively utilize PET scans when they become available. In this work, we propose a parameter-efficient multi-modal adaptation (PEMMA) framework for lightweight upgrading of a transformer-based segmentation model trained only on CT scans to also incorporate PET scans. The benefits of the proposed approach are two-fold. Firstly, we leverage the inherent modularity of the transformer architecture and perform low-rank adaptation (LoRA) of the attention weights to achieve parameter-efficient adaptation. Secondly, since the PEMMA framework attempts to minimize cross modal entanglement, it is possible to subsequently update the combined model using only one modality, without causing catastrophic forgetting of the other modality. Our proposed method achieves comparable results with the performance of early fusion techniques with just 8% of the trainable parameters, especially with a remarkable +28% improvement on the average dice score on PET scans when trained on a single modality.