基于全局-局部掩码自编码器的体积医学图像分割改进研究
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder
AI总结：
针对掩码自编码器直接用于体积医学图像存在的全局信息缺失、表征不稳定问题，提出引入全局上下文学习与一致性约束的GL-MAE自监督预训练策略，在多数据集分割任务上性能优于现有前沿方法，标注稀缺时仍有效。
摘要：
掩码自编码器（MAE）是一种极具潜力的自监督预训练技术，无需人工干预即可提升神经网络的表征学习能力。但将MAE直接应用于体积医学图像存在两大挑战：（1）缺乏对理解整体数据临床背景至关重要的全局信息；（2）无法保证从随机掩码输入中学习到的表征具有稳定性。为解决这些局限，我们提出全局-局部掩码自编码器（GL-MAE），这是一种简单却有效的自监督预训练策略。除了像现有方法那样重建被掩码的局部视图外，GL-MAE还通过重建被掩码的全局视图引入全局上下文学习。此外，该方法将完整的全局视图作为锚点，通过全局-全局一致性学习和全局-局部一致性学习引导重建过程，稳定学习进程。在多个数据集上的微调结果表明，我们的方法优于其他前沿自监督算法，凸显了其在各类体积医学图像分割任务中的有效性，即便在标注稀缺的情况下依然表现出色。我们的代码和模型将在论文录用后公开。
英文摘要：
Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention. However, applying MAE directly to volumetric medical images poses two challenges: (i) a lack of global information that is crucial for understanding the clinical context of the holistic data, (ii) no guarantee of stabilizing the representations learned from randomly masked inputs. To address these limitations, we propose the \textbf{G}lobal-\textbf{L}ocal \textbf{M}asked \textbf{A}uto\textbf{E}ncoder (GL-MAE), a simple yet effective self-supervised pre-training strategy. In addition to reconstructing masked local views, as in previous methods, GL-MAE incorporates global context learning by reconstructing masked global views. Furthermore, a complete global view is integrated as an anchor to guide the reconstruction and stabilize the learning process through global-to-global consistency learning and global-to-local consistency learning. Finetuning results on multiple datasets demonstrate the superiority of our method over other state-of-the-art self-supervised algorithms, highlighting its effectiveness on versatile volumetric medical image segmentation tasks, even when annotations are scarce. Our codes and models will be released upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
