UniBrain：基于层次化知识增强预训练的通用脑部MRI诊断
UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training
- Shanghai AI Laboratory（上海人工智能实验室）
- Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University（上海交通大学附属第六人民医院）
- Shanghai Jiao Tong University（上海交通大学）
- University of Science Technology of China（中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出层次化知识增强预训练框架UniBrain，利用24,770个影像-报告对构建多粒度对齐机制，在多个真实及公开数据集上大幅超越现有诊断方法，并在特定疾病上媲美专业放射科医生。
AI中文摘要：
磁共振成像（MRI）在脑部疾病诊断中发挥了关键作用，基于此，一系列计算机辅助人工智能方法被提出。然而，早期探索通常在单一研究中仅关注有限类型的脑部疾病，并在小规模数据上训练模型，导致泛化瓶颈。为实现更有效和可扩展的范式，我们提出了一种用于通用脑部MRI诊断的层次化知识增强预训练框架，称为UniBrain。具体而言，UniBrain利用了来自常规诊断的24,770个影像-报告对的大规模数据集。不同于以往针对单一视觉或文本特征的预训练技术，或视觉与语言信息间的强制对齐，我们利用报告信息在不同粒度上的独特特征构建层次化对齐机制，从而增强特征学习效率。UniBrain在三个存在严重类别不平衡的真实世界数据集和公开的BraTS2019数据集上进行了验证。它不仅以大幅优势持续超越所有最先进的诊断方法并提供卓越的定位性能，而且在特定疾病类型上表现出与专业放射科医生相当的性能。
英文摘要：
Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed. However, the early explorations usually focus on the limited types of brain diseases in one study and train the model on the data in a small scale, yielding the bottleneck of generalization. Towards a more effective and scalable paradigm, we propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics. Different from previous pre-training techniques for the unitary vision or textual feature, or with the brute-force alignment between vision and language information, we leverage the unique characteristic of report information in different granularity to build a hierarchical alignment mechanism, which strengthens the efficiency in feature learning. Our UniBrain is validated on three real world datasets with severe class imbalance and the public BraTS2019 dataset. It not only consistently outperforms all state-of-the-art diagnostic methods by a large margin and provides a superior grounding performance but also shows comparable performance compared to expert radiologists on certain disease types.
