Multi-instance Curriculum Learning for Histopathology Image Classifications with Hard Negative Mining and Positive Augmentation

Multi-instance learning (MIL) exhibits advanced and surpassed capabilities in understanding and recognizing complex patterns within gigapixel histopathological images. However, the currents MIL methods for the analysis of the histopathological image still give rise to two main concerns. On one hand, vanilla MIL methods intuitively focus on identifying key instances (easy-to-classify) without considering hard-to-classify instances, which is biased and prone to produce false positive instance. On the other hand, since the positive tissue occupies only a small fraction of histopathological images, it is commonly suffer from class imbalance between positive and negative instances, causing the MIL model to overly focus on the majority class. In light of these issues of bias learning, we propose a multi-instance curriculum learning method that collaboratively incorporates hard negative instance mining and positive instance augmentation to improve model’s classification performance. Specifically, we first initialize the MIL model using easy-to-classify instances, then we mine the hard negative instances (hard-to-classify) and augment the positive instances via the diffusion model. Finally, the MIL model is retrained with memory rehearsal method by combining the mined negative instances and augmented positive instances. Technically, the diffusion model is first designed to generate lesion instances, which optimally augment diverse features to reflect the realistic positive samples with post screening scenario. Extensive experimental results show that the proposed method alleviates model bias in MIL and yields improvements over the state-of-the-art methods on both public datasets and private dataset.