无需真实标签，通过偏好优化提升医学图像分割精度。
Model Agnostic Preference Optimization for Medical Image Segmentation
- 用随机丢弃生成多种分割猜测，构建偏好一致性梯度。
- 在多个数据集上边界分割更准，过拟合更少，训练更稳定。
- 适配各类网络结构，尤其适合缺乏标注的医疗场景。
偏好优化提供了一种基于相对偏好信号的可扩展监督范式，但以往在医学图像分割中的尝试仍依赖特定模型且预测样本多样性低。本文提出MAPO（模型无关偏好优化），一种利用丢弃驱动的随机分割假设构建偏好一致梯度的训练框架，无需直接真实标签。MAPO完全与架构和维度无关，支持2D/3D CNN及基于Transformer的分割流程。在多个医学数据集上的综合评估表明，相比传统监督训练，MAPO在边界贴合度、过拟合抑制和优化动态稳定性方面均有显著提升。
原文摘要 · Abstract (English)
Preference optimization offers a scalable supervision paradigm based on relative preference signals, yet prior attempts in medical image segmentation remain model-specific and rely on low-diversity prediction sampling. In this paper, we propose MAPO (Model-Agnostic Preference Optimization), a training framework that utilizes Dropout-driven stochastic segmentation hypotheses to construct preference-consistent gradients without direct ground-truth supervision. MAPO is fully architecture- and dimensionality-agnostic, supporting 2D/3D CNN and Transformer-based segmentation pipelines. Comprehensive evaluations across diverse medical datasets reveal that MAPO consistently enhances boundary adherence, reduces overfitting, and yields more stable optimization dynamics compared to conventional supervised training.
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