Norma: A Noise Robust Memory-Augmented Framework for Whole Slide Image Classification

In recent years, the Whole Slide Image (WSI) classification task has achieved great advancement due to the success of Multiple Instance Learning (MIL). However, MIL-based methods face two limitations: 1) often select the top-ranking instances of a WSI based on different metrics (e.g., attention score) to train the model due to the large resolution of WSIs, which may lead to missing global information; 2) usually consider all instances within a bag to be unordered, which will cause the local context information to be missing. To address the limitations of MIL-based methods, we formulate the WSI classification task as a long sequence classification problem in a weakly supervised setting. We propose a Noise Robust Memory-augmented (Norma) framework that serializes the WSI into an ordered sequence and caches each segment for future reuse in a sequential manner. By applying such paradiam, global and local context information of a WSI can be obtained during training. Furthermore, Normal adopts a Cyclic Training process to eliminate the noise introduced by the WSI-level labe. We obtains state-of-the-art results on CAMELYON-16, TCGA-BRAC and TCGA-LUNG datasets. We will release the code upon acceptance.