Dynamic Hypergraph Representation for Bone Metastasis Cancer Analysis
Journal:
arXiv
Published Date:
Jan 28, 2025
Abstract
Bone metastasis analysis is a significant challenge in pathology and plays a
critical role in determining patient quality of life and treatment strategies.
The microenvironment and specific tissue structures are essential for
pathologists to predict the primary bone cancer origins and primary bone cancer
subtyping. By digitizing bone tissue sections into whole slide images (WSIs)
and leveraging deep learning to model slide embeddings, this analysis can be
enhanced. However, tumor metastasis involves complex multivariate interactions
with diverse bone tissue structures, which traditional WSI analysis methods
such as multiple instance learning (MIL) fail to capture. Moreover, graph
neural networks (GNNs), limited to modeling pairwise relationships, are hard to
represent high-order biological associations. To address these challenges, we
propose a dynamic hypergraph neural network (DyHG) that overcomes the edge
construction limitations of traditional graph representations by connecting
multiple nodes via hyperedges. A low-rank strategy is used to reduce the
complexity of parameters in learning hypergraph structures, while a
Gumbel-Softmax-based sampling strategy optimizes the patch distribution across
hyperedges. An MIL aggregator is then used to derive a graph-level embedding
for comprehensive WSI analysis. To evaluate the effectiveness of DyHG, we
construct two large-scale datasets for primary bone cancer origins and
subtyping classification based on real-world bone metastasis scenarios.
Extensive experiments demonstrate that DyHG significantly outperforms
state-of-the-art (SOTA) baselines, showcasing its ability to model complex
biological interactions and improve the accuracy of bone metastasis analysis.
