SAMPO-Path: 分段意图对齐的偏好优化用于病理基础模型分段
SAMPO-Path: Segmentation Intent-Aligned Preference Optimization for Pathology Foundation Model Segmentation
AI总结：
SAMPO-Path通过偏好优化框架提升病理图像分割的准确性和临床意图一致性。
摘要：
基础模型在多对象分割中表现出强大的性能，但组织病理学图像仍然具有挑战性，因为细胞密度高、异质性高，并且像素级监督与临床分割意图之间存在差距（例如，选择性地分割特定类型的细胞核）。在实践中，这些意图通过多样且嘈杂的提示表达，导致提示-意图不一致和预测不一致。我们引入了SAMPO（具有偏好优化的分割任何模型），这是一种偏好对齐的微调框架，明确将病理基础模型与临床分割意图对齐。SAMPO是第一个将直接偏好优化（DPO）应用于纯视觉基础模型的，使模型能够从最小和不完美的提示中实现准确的分割。该框架有三个关键组成部分：（1）在线提示中心偏好挖掘，用于在提示质量之间合成偏好对；（2）多掩码偏好学习，利用输出模糊性进行细粒度排名监督；（3）结合偏好优化与像素级监督的混合损失，用于稳定训练。在两个涵盖四个任务的数据集上进行训练，并在相应的测试集和12个外部验证数据集上进行评估，SAMPO在密集的组织病理学图像中一致地提高了分割准确性、对提示变化的鲁棒性和临床意图的符合性。
英文摘要：
Foundation models have shown strong performance in multi-object segmentation with visual prompts, yet histopathology images remain challenging due to high cellular density, heterogeneity, and the gap between pixel-level supervision and clinical segmentation intent (e.g., selectively segmenting nuclei of a specific type). In practice, such intents are expressed through diverse and noisy prompts, causing prompt-intent misalignment and inconsistent predictions. We introduce SAMPO (Segmentation Anything Model with Preference Optimization), a preference-aligned fine-tuning framework that explicitly aligns pathology foundation models with clinical segmentation intent. SAMPO is the first to adapt Direct Preference Optimization (DPO) to pure vision foundation models, enabling accurate segmentation from minimal and imperfect prompts. The framework features three key components: (1) online prompt-centric preference mining to synthesize preference pairs across prompt qualities; (2) multi-mask preference learning to leverage output ambiguity for fine-grained ranking supervision; and (3) a hybrid loss combining preference optimization with pixel-level supervision for stable training. Trained on two datasets covering four tasks and evaluated on corresponding test sets and 12 external validation datasets, SAMPO consistently improves segmentation accuracy, robustness to prompt variations, and clinical intent adherence in dense histopathology images.
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。
