The Whole Pathological Slide Classification via Weakly Supervised
  Learning
Qiehe Sun Jiawen Li Jin Xu Junru Cheng Tian Guan Yonghong He
Due to its superior efficiency in utilizing annotations and addressing
gigapixel-sized images, multiple instance learning (MIL) has shown great
promise as a framework for whole slide image (WSI) classification in digital
pathology diagnosis. However, existing methods tend to focus on advanced
aggregators with different structures, often overlooking the intrinsic features
of H\&E pathological slides. To address this limitation, we introduced two
pathological priors: nuclear heterogeneity of diseased cells and spatial
correlation of pathological tiles. Leveraging the former, we proposed a data
augmentation method that utilizes stain separation during extractor training
via a contrastive learning strategy to obtain instance-level representations.
We then described the spatial relationships between the tiles using an
adjacency matrix. By integrating these two views, we designed a multi-instance
framework for analyzing H\&E-stained tissue images based on pathological
inductive bias, encompassing feature extraction, filtering, and aggregation.
Extensive experiments on the Camelyon16 breast dataset and TCGA-NSCLC Lung
dataset demonstrate that our proposed framework can effectively handle tasks
related to cancer detection and differentiation of subtypes, outperforming
state-of-the-art medical image classification methods based on MIL. The code
will be released later.