A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. To address this challenge, we propose a novel method for multi-expert prediction using diffusion models. Our method leverages the diffusion-based approach to incorporate information from multiple annotations and fuse it into a unified segmentation map that reflects the consensus of multiple experts. We evaluate the performance of our method on several datasets of medical segmentation annotated by multiple experts and compare it with state-of-the-art methods. Our results demonstrate the effectiveness and robustness of the proposed method. Our code is publicly available at https://github.com/tomeramit/Annotator-Consensus-Prediction.

翻译：医学图像分割面临的主要挑战之一是多个专家提供的标注存在较大的观察者间和观察者内部变异性。为解决这一挑战，我们提出了一种利用扩散模型进行多专家预测的新方法。该方法基于扩散机制，整合来自多个标注的信息，并将其融合为反映多位专家共识的统一分割图。我们在多个由不同专家标注的医学分割数据集上评估了该方法的性能，并将其与当前最优方法进行了比较。实验结果证明了我们方法的有效性和鲁棒性。我们的代码已公开在 https://github.com/tomeramit/Annotator-Consensus-Prediction。