aiqbench analysis
This paper proposes a variational deep-clustering method that places a Gaussian mixture prior over both spatial anatomy and Fourier spectral features, learning prototypes from unlabeled brain MRI. The reported gains (up to 94% better cluster separation over spatial-only baselines, lower seed variance, 0.28 ms inference) are measured on a single dataset against self-selected baselines, so they need independent replication on other datasets and stronger baselines. For ML readers, the useful idea is the joint-prior-in-decoder formulation with end-to-end EM training, which avoids two-stage clustering.
Dual-VaDE: Efficient and Robust Prototype Learning via Variational Dual-Domain Mixture Priors
arXiv:2611.05099v1 Announce Type: new Abstract: In this paper, we propose Dual-VaDE, an unsupervised generative framework for learning interpretable anatomical prototypes from unlabeled medical images. In contrast to previous variational methods that rely on a single spatial latent code, which often entangles global geometry with local textures and fails to capture their cross-domain dependencies, Dual-VaDE introduces a dual-domain Gaussian mixture prior that explicitly couples spatial anatomy with phase-preserving Fourier spectral representations. We derive an exact, closed-form variational lower bound that fuses this joint prior into the decoder and optimizes the entire network end-to-end via an alternating EM formulation, avoiding heuristic two-stage clustering while ensuring superior training stability and robustness. Evaluated on OASIS-3 brain MRI, Dual-VaDE delivers substantially sharper and more plausible prototypes, achieves superior cluster separation with improvements of up to 94% over spatial-only baselines, exhibits markedly reduced variance across random seeds, and incurs negligible inference overhead (0.28 ms per image).
Abstract:In this paper, we propose Dual-VaDE, an unsupervised generative framework for learning interpretable anatomical prototypes from unlabeled medical images. In contrast to previous variational methods that rely on a single spatial latent code, which often entangles global geometry with local textures and fails to capture their cross-domain dependencies, Dual-VaDE introduces a dual-domain Gaussian mixture prior that explicitly couples spatial anatomy with phase-preserving Fourier spectral representations. We derive an exact, closed-form variational lower bound that fuses this joint prior into the decoder and optimizes the entire network end-to-end via an alternating EM formulation, avoiding heuristic two-stage clustering while ensuring superior training stability and robustness. Evaluated on OASIS-3 brain MRI, Dual-VaDE delivers substantially sharper and more plausible prototypes, achieves superior cluster separation with improvements of up to 94% over spatial-only baselines, exhibits markedly reduced variance across random seeds, and incurs negligible inference overhead (0.28 ms per image).
Machine Learning (cs.LG)
arXiv:2611.05099 [cs.LG]
(or arXiv:2611.05099v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.05099 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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