保留什么，适配何处：妇科图像持续分割中遗忘的深度分析

中文摘要 AI 辅助

医学图像分割模型通常基于所有数据可同时获取的假设进行训练，但在临床实践中，数据集往往是按顺序到达的，要求模型不断适应不断变化的数据分布。我们在妇科图像分割中研究该问题，该场景中成像模态、解剖结构和标注协议存在显著异质性，构成极具挑战性的持续学习场景。在这些大规模分布偏移下，现有持续学习方法难以保留先前学习的知识，导致灾难性遗忘。为更好理解该场景下的遗忘问题，我们研究编码器-解码器的不同区域如何影响妇科持续分割中的性能与遗忘。通过分块消融分析，我们发现消融早期编码器和晚期解码器区域会导致最大的性能下降，表明分割性能在网络层级中的依赖并不均匀。通过受控适配实验，我们进一步发现，当更新仅限制在瓶颈相邻区域时，遗忘仍保持有限；但一旦较浅的编码器和解码器可训练，即使仅更新一小部分参数，遗忘也会急剧增加。这些发现表明，编码器-解码器架构中的遗忘在持续学习期间受网络深度上的更新位置强烈影响。完整代码和分析管道将在录用后公开。

英文摘要

The clinical management of gynecological diseases often relies on medical imaging for diagnosis, treatment planning, and follow-up. Segmentation in this setting is challenging because successive tasks may differ in imaging modality, target anatomy, pathology, and annotation structure. Continual learning allows models to adapt to new tasks without simultaneous access to previous datasets. However, when successive tasks differ substantially, learning a new task can degrade performance on earlier ones, a problem known as catastrophic forgetting. Understanding where adaptation disrupts previous knowledge can help guide the design of more targeted continual-learning strategies. We investigate how forgetting changes as different parts of an encoder--decoder network are allowed to adapt. We progressively expand the trainable region of a 3D nnU-Net backbone from the bottleneck toward input- and output-proximal blocks. Under a shared learning rate, adaptation near the bottleneck largely preserves previous-task performance but provides limited current-task learning, whereas broader adaptation improves current-task performance but sharply increases forgetting. This trade-off persists even when the average change in trainable backbone parameters is approximately comparable. Assigning different learning rates to different blocks substantially reduces forgetting when part of the backbone is trainable, although this changes both the size and location of the updates. Forgetting still increases as more blocks are trained and remains severe when the full backbone is updated. These results show that forgetting depends not only on how much the model changes, but also on which parts of the model are allowed to change.

发表机构

• Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学（MBZUAI））

机构由 AI 辅助整理，请以论文原文为准。