双向多实例学习中的去噪互知识蒸馏
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning
AI总结：
针对多实例学习在全切片图像分类中可能引入噪声标签的问题，本文提出一种通过弱到强泛化技术增强伪标签校正能力的去噪互知识蒸馏方法，在包级和实例级预测上均提升了双级MIL算法的性能。
摘要：
多实例学习是数字病理学中全切片图像分类的主要方法，它允许使用切片级别的标签来监督模型训练。尽管MIL消除了监督学习中繁琐的细粒度标注过程，但它能否学习到准确的包级和实例级分类器仍是一个问题。为了解决这个问题，研究者引入了实例级分类器和实例掩码，将预测基于支持性补丁。这些方法虽然在实际中提升了MIL方法的性能，但可能引入噪声标签。我们提出通过弱到强泛化技术引发的伪标签校正能力来增强包级和实例级学习过程，从而弥合常用MIL与全监督学习之间的差距。所提出的算法在包级和实例级预测上均提升了双级MIL算法的性能。在公共病理数据集上的实验展示了所提出方法的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
