LoRA-based methods on Unet for transfer learning in Subarachnoid Hematoma Segmentation

TL;DR

This study explores LoRA-based transfer learning methods applied to Unet architectures for segmenting aneurysmal subarachnoid hemorrhage in CT scans, demonstrating improved performance over standard fine-tuning, especially with larger hemorrhages.

Contribution

Introduces novel CP-LoRA and DoRA variants for parameter-efficient transfer learning in medical image segmentation, outperforming existing LoRA methods and standard fine-tuning.

Findings

LoRA methods outperform standard fine-tuning in accuracy.

Performance improves with larger hemorrhage volumes.

CP-LoRA achieves similar results with fewer parameters.

Abstract

Aneurysmal subarachnoid hemorrhage (SAH) is a life-threatening neurological emergency with mortality rates exceeding 30%. Transfer learning from related hematoma types represents a potentially valuable but underexplored approach. Although Unet architectures remain the gold standard for medical image segmentation due to their effectiveness on limited datasets, Low-Rank Adaptation (LoRA) methods for parameter-efficient transfer learning have been rarely applied to convolutional neural networks in medical imaging contexts. We implemented a Unet architecture pre-trained on computed tomography scans from 124 traumatic brain injury patients across multiple institutions, then fine-tuned on 30 aneurysmal SAH patients from the University of Michigan Health System using 3-fold cross-validation. We developed a novel CP-LoRA method based on tensor CP-decomposition and introduced DoRA variants (DoRA-C, convDoRA, CP-DoRA) that decompose weight matrices into magnitude and directional components. We compared these approaches against existing LoRA methods (LoRA-C, convLoRA) and standard fine-tuning strategies across different modules on a multi-view Unet model. LoRA-based methods consistently outperformed standard Unet fine-tuning. Performance varied by hemorrhage volume, with all methods showing improved accuracy for larger volumes. CP-LoRA achieved comparable performance to existing methods while using significantly fewer parameters. Over-parameterization with higher ranks consistently yielded better performance than strictly low-rank adaptations. This study demonstrates that transfer learning between hematoma types is feasible and that LoRA-based methods significantly outperform conventional Unet fine-tuning for aneurysmal SAH segmentation.