无模型偏好优化用于医学图像分割
Model Agnostic Preference Optimization for Medical Image Segmentation
AI总结：
MAPO通过Dropout驱动的随机分割假设实现无模型偏好优化，提升医学图像分割的边界遵循性和稳定性。
摘要：
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，但先前的医学图像分割尝试仍然模型特定，并依赖低多样性预测采样。在本文中，我们提出了MAPO（无模型偏好优化），一种训练框架，它利用Dropout驱动的随机分割假设来构建偏好一致的梯度，而无需直接的地面真相监督。MAPO完全无关架构和维度，支持2D/3D CNN和Transformer基于的分割管道。在多样化的医学数据集上的全面评估表明，MAPO一致地提高了边界遵循性，减少了过拟合，并产生了比传统监督训练更稳定的优化动态。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
