论文标题
多次实例通过迭代自定进度的监督对比学习学习
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
论文作者
论文摘要
在只有行李级标签时，个人实例的学习表示形式是多个实例学习（MIL）的基本挑战。最近的作品显示了使用对比的自我监督学习（CSSL）的令人鼓舞的结果，该学习学会推动与两个不同随机选择实例相对应的表示形式。不幸的是，在现实世界中的应用程序（例如医学图像分类）中，通常存在类不平衡，因此随机选择的实例主要属于同一多数类，这使CSSL无法从学习类间差异。为了解决这个问题，我们提出了一个新颖的框架，迭代的自定进度的监督对比度学习（ITS2CLR），该学习通过利用从行李级标签中得出的实例级伪标签来改善学习的表示形式。该框架采用了一种新颖的自定进度抽样策略来确保伪标签的准确性。我们在三个医疗数据集上评估ITS2CLR，表明它提高了实例级伪标签和表示的质量，并且在袋子和实例级别的准确性方面都超过了现有的MIL方法。代码可从https://github.com/kangningthu/its2clr获得
Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown promising results using contrastive self-supervised learning (CSSL), which learns to push apart representations corresponding to two different randomly-selected instances. Unfortunately, in real-world applications such as medical image classification, there is often class imbalance, so randomly-selected instances mostly belong to the same majority class, which precludes CSSL from learning inter-class differences. To address this issue, we propose a novel framework, Iterative Self-paced Supervised Contrastive Learning for MIL Representations (ItS2CLR), which improves the learned representation by exploiting instance-level pseudo labels derived from the bag-level labels. The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels. We evaluate ItS2CLR on three medical datasets, showing that it improves the quality of instance-level pseudo labels and representations, and outperforms existing MIL methods in terms of both bag and instance level accuracy. Code is available at https://github.com/Kangningthu/ItS2CLR
