CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology Image Representation

Predicting survival rates based on multi-gigapixel histopathology images is one of the most challenging tasks in digital pathology. Due to the computational complexities, Multiple Instance Learning (MIL) has become the conventional approach for this process as it breaks the image into smaller patches. However, this technique fails to account for the individual cells present in each patch, while they are the fundamental part of the tissue. In this work, we developed a novel dynamic and hierarchical point-cloud-based method (CO-PILOT) for the processing of cellular graphs extracted from routine histopathology images. By using bottom-up information propagation and top-down conditional attention, our model gains access to an adaptive focus across different levels of tissue hierarchy. Through comprehensive experiments, we demonstrate that our model can outperform all the state-of-the-art methods in survival prediction, including the hierarchical Vision Transformer (ViT), across three datasets and four metrics with only half of the parameters of the closest baseline. Importantly, our model is able to stratify the patients into different risk cohorts with statistically different outcomes across three large datasets, a task that was previously achievable only using genomic information. Furthermore, we publish a large dataset containing 873 cellular graphs from 188 patients, along with their survival information, making it one of the largest publicly available datasets in this context.