通过三阶段渐进式学习，让模型从自动分割逐步适应用户涂改，提升全身PET/CT病灶交互分割精度。
Three-Phase Scribble-Adaptive Curriculum Learning for autoPETV Grand Challenge
- 用笔迹编码作为额外输入通道，分三阶段渐进训练网络：先自动分割，再看真值笔迹，最后自纠错。
- 在六步交互的五折交叉验证中，平均AUC-Dice达3.836，首步修正贡献近半提升。
- 适合医疗影像交互分割研究者，代码与模型开源，可直接用于临床辅助诊断系统开发。
本报告介绍李博张针对全身影像PET/CT中病灶交互分割挑战所提出的算法解决方案。交互信息以累积前景与背景涂改笔迹的形式编码为两个额外输入通道，采用约1400万参数的残差编码器U-Net，在4000个训练周期内进行三阶段课程学习：首先在无交互条件下学习全自动分割；其次在随机采样的可见性模式下观察真值引导的笔迹；最后通过在线模拟最多五步错误驱动修正来适应自身预测偏差。训练数据涵盖1811例autoPET和DeepPSMA病例，提交结果通过五折最佳及最终检查点的对数平均集成。在六步交互的五折交叉验证中，最终检查点达到平均AUC-Dice 3.836与平均AUC-DMM 3.869，各折表现持续提升，其中约一半增益由首个修正笔迹带来。代码与训练模型已公开于https://github.com/Libo1023/autoPETV-Curriculum。
原文摘要 · Abstract (English)
This report describes Libo Zhang's algorithmic solution to autoPETV Grand Challenge on interactive lesion segmentation in whole-body PET/CT. Interaction is encoded as two additional input channels that rasterize the accumulated foreground and background scribbles, and a residual-encoder U-Net of about 140 million parameters is trained with a three-phase curriculum over 4000 epochs: the network first learns fully automatic segmentation with silent interaction channels, then observes ground-truth-derived scribbles under randomly sampled visibility modes, and finally adapts to its own mistakes through online simulation of up to five error-driven correction steps. Training draws on 1811 autoPET and DeepPSMA studies, and the submission ensembles the best and final checkpoints of five folds by logit averaging. In interactive five-fold cross-validation with six interaction steps, the final checkpoints reach a mean AUC-Dice of 3.836 and a mean AUC-DMM of 3.869, improving monotonically in every fold, with roughly half of the total gain delivered by the first corrective scribble. Our code and trained model checkpoints are available on https://github.com/Libo1023/autoPETV-Curriculum.
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