Interactive segmentation, an integration of AI algorithms and human
expertise, premises to improve the accuracy and efficiency of curating
large-scale, detailed-annotated datasets in healthcare. Human experts revise
the annotations predicted by AI, and in turn, AI improves its predictions by
learning from these revised annotations. This interactive process continues to
enhance the quality of annotations until no major revision is needed from
experts. The key challenge is how to leverage AI predicted and expert revised
annotations to iteratively improve the AI. Two problems arise: (1) The risk of
catastrophic forgetting--the AI tends to forget the previously learned classes
if it is only retrained using the expert revised classes. (2) Computational
inefficiency when retraining the AI using both AI predicted and expert revised
annotations; moreover, given the dominant AI predicted annotations in the
dataset, the contribution of newly revised annotations--often account for a
very small fraction--to the AI training remains marginal. This paper proposes
Continual Tuning to address the problems from two perspectives: network design
and data reuse. Firstly, we design a shared network for all classes followed by
class-specific networks dedicated to individual classes. To mitigate
forgetting, we freeze the shared network for previously learned classes and
only update the class-specific network for revised classes. Secondly, we reuse
a small fraction of data with previous annotations to avoid over-computing. The
selection of such data relies on the importance estimate of each data. The
importance score is computed by combining the uncertainty and consistency of AI
predictions. Our experiments demonstrate that Continual Tuning achieves a speed
16x greater than repeatedly training AI from scratch without compromising the
performance.

交互分割是集成人工智能算法和人类专业知识的方法，可以提高医疗卫生领域大规模、详细标注数据集的准确性和效率。本文提出了连续调整方法来解决 AI 预测和专家修订标注之间的两个问题，即灾难性遗忘和计算效率问题，通过网络设计和数据重复使用来改进 AI 的训练，实验证明连续调整方法在提高性能的同时也提高了训练速度。