Improved Generalizability in Medical Computer Vision: Hyperbolic Deep Learning in Multi-Modality Neuroimaging.

Area of Science:

• Medical imaging analysis
• Geometric deep learning
• Neuroimaging diagnostics

Background:

• Deep learning models for radiological diagnostics often lack generalizability to new datasets.
• Geometric deep learning, utilizing non-Euclidean space principles, may enhance model generalizability.
• Hyperbolic convolutional neural networks (HCNNs) are an emerging geometric deep learning approach.

Purpose of the Study:

• To compare the performance of HCNNs against traditional CNNs in neuroimaging tasks.
• To evaluate HCNNs' generalizability, robustness to adversarial attacks, and semantic organization.
• To assess HCNNs' effectiveness in zero-shot evaluations for conditions like ischemic stroke.

Main Methods:

• Comparative analysis of HCNNs and CNNs across diverse medical imaging modalities and diseases.
• Assessment of model performance parity, adversarial robustness, embedding space organization, and generalizability.
• Inclusion of zero-shot evaluations using ischemic stroke non-contrast CT images.

Main Results:

• HCNNs matched CNNs in simpler tasks but showed superior semantic organization and adversarial robustness.
• HCNNs demonstrated equal performance to CNNs in identifying Alzheimer's disease on out-of-sample datasets.
• HCNNs outperformed both CNNs and radiologists in zero-shot evaluations for ischemic stroke detection.

Conclusions:

• HCNNs offer enhanced robustness and semantic organization for neuroimaging data, contributing to improved generalizability.
• While HCNNs show potential, challenges remain in efficiency and performance with larger, complex datasets, necessitating further architectural optimization.
• HCNNs represent a promising advancement for generalizable deep learning models in medical imaging and neuroimaging diagnostics, particularly under adversarial conditions.