标题： 只需说更好或更差：一种无需手动注释的人机协作医学图像分割框架 显示英文标题

标题： Just Say Better or Worse: A Human-AI Collaborative Framework for Medical Image Segmentation Without Manual Annotations

摘要： 人工标注医学图像是一项劳动密集且耗时的过程，这在开发和部署稳健的医学影像人工智能系统中构成了重大瓶颈。 本文介绍了一种新颖的人机协作框架，用于医学图像分割，该框架通过消除显式手动像素级标记的需求，显著减少了标注负担。 核心创新在于一种偏好学习范式，其中人类专家提供最少的、直观的反馈——只需指出AI生成的分割结果是否比之前版本更好或更差。 该框架包含四个关键组件：(1) 一个可适应的基础模型（FM）用于特征提取，(2) 基于特征相似性的标签传播，(3) 一个点击代理，它从人类的更好或更差反馈中学习，以决定在哪里点击以及使用哪种标签，(4) 一个多轮分割学习过程，利用点击代理和FM基础的标签传播生成的伪标签来训练最先进的分割网络。 在三个公开数据集上的实验表明，所提出的方法仅使用二元偏好反馈即可实现具有竞争力的分割性能，而无需专家直接手动标注图像。 显示英文摘要

摘要： Manual annotation of medical images is a labor-intensive and time-consuming process, posing a significant bottleneck in the development and deployment of robust medical imaging AI systems. This paper introduces a novel Human-AI collaborative framework for medical image segmentation that substantially reduces the annotation burden by eliminating the need for explicit manual pixel-level labeling. The core innovation lies in a preference learning paradigm, where human experts provide minimal, intuitive feedback -- simply indicating whether an AI-generated segmentation is better or worse than a previous version. The framework comprises four key components: (1) an adaptable foundation model (FM) for feature extraction, (2) label propagation based on feature similarity, (3) a clicking agent that learns from human better-or-worse feedback to decide where to click and with which label, and (4) a multi-round segmentation learning procedure that trains a state-of-the-art segmentation network using pseudo-labels generated by the clicking agent and FM-based label propagation. Experiments on three public datasets demonstrate that the proposed approach achieves competitive segmentation performance using only binary preference feedback, without requiring experts to directly manually annotate the images.