Geometric Deep Learning Methods for Improved Generalizability in Medical Computer Vision: Hyperbolic Convolutional Neural Networks in Multi-Modality Neuroimaging

ObjectiveThis study investigates the potential advantages of hyperbolic convolutional neural networks (HCNNs) over traditional convolutional neural networks (CNNs) in neuroimaging tasks. Materials and MethodsWe conducted a comparative analysis of HCNNs and CNNs across various medical imaging modalities and diseases, with a focus on a compiled multi-modality neuroimaging dataset. The models were assessed for performance parity, robustness to adversarial attacks, semantic organization of embedding spaces, and generalizability. Zero-shot evaluations were also performed with ischemic stroke non-contrast CT images. ResultsHCNNs matched CNN performance on less complex settings and demonstrated superior semantic organization, and robustness to adversarial attacks. While HCNNs equaled CNNs in out-of-sample datasets identifying Alzheimers disease, in zero-shot evaluations, HCNNs outperformed CNNs and radiologists. DiscussionHCNNs deliver enhanced robustness and organization in the neuroimaging data. This likely underlies why while HCNNs perform similarly to CNNs with respect to in-sample tasks, they confer improved generalizability. Nevertheless, HCNNs encounter efficiency and performance challenges with larger, complex datasets. These limitations underline the need for further optimization of HCNN architectures. ConclusionHCNNs present promising improvements in generalizability and resilience for medical imaging applications, particularly in neuroimaging. Despite challenges with larger datasets, HCNNs enhance performance under adversarial conditions and offer better semantic organization, suggesting valuable potential in generalizable deep learning models in medical imaging and neuroimaging diagnostics.