[论文解读] Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images

Guang Li, Ren Togo+2 more|arXiv (Cornell University)|Apr 7, 2021

Traditional Chinese Medicine Studies参考文献 38被引用 9

一句话总结

本文提出了一种基于教师-学生架构的新型自监督学习方法，结合跨视图和跨模型损失，从胃部X光图像中学习具有判别性的表征以实现胃炎检测。该方法仅使用40例标注患者病例，即取得了0.931的调和平均分数，性能优于五种自监督学习方法和三种先前学习方法。

ABSTRACT

Purpose: Manual annotation of gastric X-ray images by doctors for gastritis detection is time-consuming and expensive. To solve this, a self-supervised learning method is developed in this study. The effectiveness of the proposed self-supervised learning method in gastritis detection is verified using a few annotated gastric X-ray images. Methods: In this study, we develop a novel method that can perform explicit self-supervised learning and learn discriminative representations from gastric X-ray images. Models trained based on the proposed method were fine-tuned on datasets comprising a few annotated gastric X-ray images. Five self-supervised learning methods, i.e., SimSiam, BYOL, PIRL-jigsaw, PIRL-rotation, and SimCLR, were compared with the proposed method. Furthermore, three previous methods, one pretrained on ImageNet, one trained from scratch, and one semi-supervised learning method, were compared with the proposed method. Results: The proposed method's harmonic mean score of sensitivity and specificity after fine-tuning with the annotated data of 10, 20, 30, and 40 patients were 0.875, 0.911, 0.915, and 0.931, respectively. The proposed method outperformed all comparative methods, including the five self-supervised learning and three previous methods. Experimental results showed the effectiveness of the proposed method in gastritis detection using a few annotated gastric X-ray images. Conclusions: This paper proposes a novel self-supervised learning method based on a teacher-student architecture for gastritis detection using gastric X-ray images. The proposed method can perform explicit self-supervised learning and learn discriminative representations from gastric X-ray images. The proposed method exhibits potential clinical use in gastritis detection using a few annotated gastric X-ray images.

研究动机与目标

• 解决专家标注胃部X光图像在胃炎检测中成本高、耗时长的问题。
• 开发一种自监督学习方法，无需依赖人工标注即可学习具有判别性的表征。
• 仅使用少量标注的胃部X光图像提升检测性能。
• 验证所提方法相对于现有自监督和监督基线方法的有效性。
• 展示在标注数据有限的情况下，自监督表征学习在医学影像中的临床潜力。

提出的方法

• 采用教师-学生神经网络架构，其中学生网络处理同一胃部X光图像的两个增强视图。
• 跨视图损失最小化学生网络中两个增强视图表征之间的距离。
• 跨模型损失最小化教师和学生网络中同一视图表征之间的距离。
• 学生网络使用多层感知机（MLP）头将特征投影用于对比学习。
• 使用这两种损失在大量未标注胃部X光图像上进行模型预训练。
• 预训练完成后，模型在10至40例患者的少量专家标注数据集上进行微调。

实验结果

研究问题

• RQ1 结合跨视图和跨模型损失的自监督学习能否有效从胃部X光图像中学习到具有判别性的表征？
• RQ2 所提方法在胃炎检测中与现有自监督学习方法（如SimSiam、BYOL、SimCLR）相比表现如何？
• RQ3 在仅使用少量标注胃部X光图像的情况下，所提方法能在多大程度上实现高性能检测？
• RQ4 当在有限标注数据上进行微调时，所提方法是否优于监督和半监督基线方法？
• RQ5 预训练的自监督编码器能否在极少标注工作量下有效迁移到患者级别的胃炎检测？

主要发现

• 所提方法在10例标注患者病例下取得0.875的调和平均分数，提升至40例时达到0.931。
• 该方法优于五种自监督学习方法：SimSiam、BYOL、PIRL-jigsaw、PIRL-rotation和SimCLR。
• 同时超越了三种先前方法：ImageNet预训练、随机权重训练模型以及一种半监督学习模型。
• 跨视图和跨模型损失实现了显式的自监督学习，并提升了在复杂胃部X光图像上的特征判别能力。
• 结果表明，该方法在标注需求极少的情况下，对胃炎检测具有临床有效性。
• 据作者所知，这是首个使用胃部X光图像验证自监督学习在胃炎检测中应用的研究。