Title: From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Histopathology whole slide image analysis, WSI dataset, survival prediction

Abstract: It is clinically crucial and potentially beneficial to analyze and directly model the spatial distributions of cells in histopathology whole slide images (WSI). However, existing methods typically analyze WSIs via image representation learning and ignore the importance of cell distributions. Thus, it remains an open question whether deep learning models can directly and effectively analyze WSIs from the semantic aspect of cell distributions. In this work, we argue that each WSI can be regarded as a collection of cells and propose a new scheme consisting of cell detection and cell cloud modeling to tackle these challenges. Firstly, we propose a novel human-in-the-loop label refinement method to finetune the pretrained cell detection and classification model. Then, a novel hierarchical Cell Cloud Transformer (CCFormer) is proposed to model the cell spatial distribution. Specifically, a Neighboring Information Embedding module is proposed to characterize the distribution of cells within the cell neighborhood, and a Hierarchical Spatial Perception module is proposed to learn the spatial relationship among cells in a bottom-up manner. Clinical analysis indicates that clinical evaluation metrics directly based on counting cells can effectively assess patients' survival risk, offering significant potential for analyzing and modeling cell distribution in WSIs. Besides, extensive experiments on survival prediction and cancer staging show that CCFormer achieves state-of-the-art performances and evidently outperforms other competing methods by learning from cell spatial distribution alone.