SeLoRA: Self-expanding LoRA for high-quality and efficient medical image synthesis

The persistent challenge of medical image synthesis posed by the scarcity of annotated data and the need to synthesize “missing modalities” for multi-modal analysis, underscores the imperative development of effective synthesis methods. Recently, the combination of Low-Rank Adaptation (LoRA) with latent diffusion models (LDMs) has emerged as a viable approach for efficiently adapting pre-trained large language models, in the medical field. However, the direct application of LoRA assumes uniform ranking across all linear layers, overlooking the significance of different weight matrices, and leading to sub-optimal outcomes. Prior works on LoRA prioritize the reduction of trainable parameters, and there exists an opportunity to further tailor this adaptation process to the intricate demands of medical image synthesis. In response, we present SeLoRA, a Self-Expanding Low-Rank Adaptation module, that dynamically expands its ranking across layers during training, strategically placing additional ranks on crucial layers, to allow the model to elevate synthesis quality where it matters most. The proposed method not only enables LDMs to fine-tune on medical data efficiently, but also empowers the model to achieve improved image quality with minimal ranking. The code of our SeLoRA method is publicly available at https://anonymous.4open.science/r/SeLoRA-980D this link.