VAEsselSparse applies sparse convolutions and attention in a VAE to achieve 8x8x8 spatial compression of organ-scale vascular data while preserving reconstruction quality and clinically useful features for classification and generation.
The Lancet Digital Health4(4), e256–e265 (2022)
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A pipeline using segmentation, atlas registration, radiomics, and geometric features achieves 87.5% CVD classification accuracy on ASOCA, outperforming direct raw-image classification at 67.5%.
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Sparse Representation Learning for Vessels
VAEsselSparse applies sparse convolutions and attention in a VAE to achieve 8x8x8 spatial compression of organ-scale vascular data while preserving reconstruction quality and clinically useful features for classification and generation.