用伪标签修正提升病理切片分类的双层学习效果
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning
- 双向MIL中引入去噪伪标签修正机制
- 在公开病理数据集上实现双层预测性能提升
- 适合关注医学图像细粒度分析的研究者
多实例学习是数字病理学中全切片图像分类的主要方法，可利用切片级标签监督模型训练。尽管MIL避免了精细标注的繁琐过程，但其能否学习到准确的包级与实例级分类器仍存疑问。为此，研究引入实例级分类器和实例掩码，使预测基于支持性切片。这些方法虽提升了MIL性能，但可能引入噪声标签。本文提出通过弱到强泛化技术，为包级与实例级学习过程赋予伪标签修正能力，弥合传统MIL与全监督学习的差距。所提算法在双层MIL任务中显著提升包级与实例级预测性能。在公开病理数据集上的实验验证了该方法的优势。
原文摘要 · Abstract (English)
Multiple Instance Learning is the predominant method for Whole Slide Image classification in digital pathology, enabling the use of slide-level labels to supervise model training. Although MIL eliminates the tedious fine-grained annotation process for supervised learning, whether it can learn accurate bag- and instance-level classifiers remains a question. To address the issue, instance-level classifiers and instance masks were incorporated to ground the prediction on supporting patches. These methods, while practically improving the performance of MIL methods, may potentially introduce noisy labels. We propose to bridge the gap between commonly used MIL and fully supervised learning by augmenting both the bag- and instance-level learning processes with pseudo-label correction capabilities elicited from weak to strong generalization techniques. The proposed algorithm improves the performance of dual-level MIL algorithms on both bag- and instance-level predictions. Experiments on public pathology datasets showcase the advantage of the proposed methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。