构建多器官医学CT重建数据集，提升模型泛化能力。
MORE: Multi-Organ Medical Image REconstruction Dataset
- 构建涵盖9个器官、15类病灶的多源异构CT数据集
- 基于该数据集的模型在未见器官上表现优于现有方法
- 适合医疗影像重建、跨器官泛化研究者使用
CT重建为放射科医生提供诊断与治疗图像，但当前深度学习方法通常局限于特定解剖结构和数据集，难以泛化到未见解剖结构和病灶。为此，我们提出了多器官医学图像重建（MORE）数据集，包含9种不同解剖结构及15种病灶类型的CT扫描。该数据集旨在实现两大目标：(1) 支持深度学习模型在广泛异构数据上的稳健训练；(2) 促进对模型泛化能力的严格评估。我们还建立了一个强基线方案，在这些挑战性条件下优于先前方法。结果表明：(1) 全面数据集有助于提升模型泛化能力；(2) 基于优化的方法在未见解剖结构上具备更强鲁棒性。MORE数据集可免费获取，协议为CC-BY-NC 4.0，详见项目主页 https://more-med.github.io/
原文摘要 · Abstract (English)
CT reconstruction provides radiologists with images for diagnosis and treatment, yet current deep learning methods are typically limited to specific anatomies and datasets, hindering generalization ability to unseen anatomies and lesions. To address this, we introduce the Multi-Organ medical image REconstruction (MORE) dataset, comprising CT scans across 9 diverse anatomies with 15 lesion types. This dataset serves two key purposes: (1) enabling robust training of deep learning models on extensive, heterogeneous data, and (2) facilitating rigorous evaluation of model generalization for CT reconstruction. We further establish a strong baseline solution that outperforms prior approaches under these challenging conditions. Our results demonstrate that: (1) a comprehensive dataset helps improve the generalization capability of models, and (2) optimization-based methods offer enhanced robustness for unseen anatomies. The MORE dataset is freely accessible under CC-BY-NC 4.0 at our project page https://more-med.github.io/
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