让大模型和小模型互相纠错，提升医学图像分割精度。
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation
- 大模型与小模型协同训练，互相修正错误预测。
- 在前列腺数据集上，Dice分数提升10.31%。
- 适合需要高精度的医学图像分割任务。
大型预训练视觉基础模型具有出色的泛化能力，但在特定领域下游任务中，其固有的先验知识可能成为双刃剑。在存在域偏移的半监督医学图像分割场景下，如MedSAM这样的基础模型容易产生过度自信的错误预测，导致误差累积，阻碍未标记数据的有效利用并限制性能提升。本文提出一种基础模型与常规模型协同训练框架（SynFoC），观察到从头训练的常规模型能纠正基础模型的高置信度误判，而基础模型则可在早期阶段为常规模型提供高质量伪标签。为进一步增强两者协同训练效果并促进可靠收敛，提出一致性正则化策略。实验在四个公开多域数据集上验证了方法优越性，尤其在前列腺数据集上，Dice分数提升10.31%。代码已开源。
原文摘要 · Abstract (English)
Large pretrained visual foundation models exhibit impressive general capabilities. However, the extensive prior knowledge inherent in these models can sometimes be a double-edged sword when adapting them to downstream tasks in specific domains. In the context of semi-supervised medical image segmentation with domain shift, foundation models like MedSAM tend to make overconfident predictions, some of which are incorrect. The error accumulation hinders the effective utilization of unlabeled data and limits further improvements. In this paper, we introduce a Synergistic training framework for Foundation and Conventional models (SynFoC) to address the issue. We observe that a conventional model trained from scratch has the ability to correct the high-confidence mispredictions of the foundation model, while the foundation model can supervise it with high-quality pseudo-labels in the early training stages. Furthermore, to enhance the collaborative training effectiveness of both models and promote reliable convergence towards optimization, the consensus-divergence consistency regularization is proposed. We demonstrate the superiority of our method across four public multi-domain datasets. In particular, our method improves the Dice score by 10.31\% on the Prostate dataset. Our code is available at https://github.com/MQinghe/SynFoC .
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