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.
Abstract (translated)
动脉瘤性蛛网膜下腔出血（SAH）是一种危及生命的神经紧急情况，死亡率超过30%。从相关血肿类型中进行迁移学习代表了一种潜在但尚未充分探索的方法。尽管Unet架构由于在有限数据集上的有效性而仍然是医学图像分割的金标准，但在医学成像上下文中，用于参数高效迁移学习的低秩适应（LoRA）方法却很少被应用到卷积神经网络上。我们基于来自124名创伤性脑损伤患者的计算机断层扫描图像进行预训练，并在密歇根大学医疗系统的30例动脉瘤性SAH患者数据集上使用三折交叉验证进行了微调。 我们开发了一种新的CP-LoRA方法，该方法基于张量CP分解，并引入了DoRA变体（DoRA-C、convDoRA和CP-DoRA），这些变体将权重矩阵分解为幅度和方向成分。我们在一个多视图Unet模型的不同模块上比较了这些方法与现有LoRA方法（LoRA-C、convLoRA）以及标准微调策略的性能。 基于LoRA的方法始终优于标准Unet微调方法。性能根据出血体积而变化，所有方法对于较大体积都显示出更高的准确性。CP-LoRA在使用显著较少参数的情况下达到了与现有方法相当的表现。使用更高秩进行过参数化总是比严格低秩适应表现出更好的性能。 本研究证明了血肿类型之间的迁移学习是可行的，并且基于LoRA的方法在动脉瘤性SAH分割中明显优于传统的Unet微调方法。
URL
https://arxiv.org/abs/2508.01772
