用可选聚合注意力机制，从多张肠镜图中判断溃疡性结肠炎严重程度
Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer
- 设计选择性聚合的Transformer，从患者多张图像中提取严重病灶特征
- 在两个数据集上优于现有方法，提升相邻严重等级区分能力
- 适配真实临床场景，适合医疗图像分析与炎症性肠病研究者
溃疡性结肠炎（UC）的患者级严重程度诊断在真实临床中常见，以患者最严重病灶评分为准。但以往基于图像级估计的方法假设输入为单张图像，无法利用临床实际记录的多图严重度标签。本文提出一种基于选择性聚合注意力机制的变压器模型，从同一患者多张图像中估计整体严重程度，更贴近临床实际。该方法能有效聚合各图像中严重区域的特征，增强相邻严重等级间的判别能力。实验表明，在两个数据集上优于当前最优的多实例学习方法；在真实临床环境中验证也显示其性能超越传统图像级方法。代码已开源。
原文摘要 · Abstract (English)
Patient-level diagnosis of severity in ulcerative colitis (UC) is common in real clinical settings, where the most severe score in a patient is recorded. However, previous UC classification methods (i.e., image-level estimation) mainly assumed the input was a single image. Thus, these methods can not utilize severity labels recorded in real clinical settings. In this paper, we propose a patient-level severity estimation method by a transformer with selective aggregator tokens, where a severity label is estimated from multiple images taken from a patient, similar to a clinical setting. Our method can effectively aggregate features of severe parts from a set of images captured in each patient, and it facilitates improving the discriminative ability between adjacent severity classes. Experiments demonstrate the effectiveness of the proposed method on two datasets compared with the state-of-the-art MIL methods. Moreover, we evaluated our method in real clinical settings and confirmed that our method outperformed the previous image-level methods. The code is publicly available at https://github.com/Shiku-Kaito/Ordinal-Multiple-instance-Learning-for-Ulcerative-Colitis-Severity-Estimation.
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