Flip Distribution Alignment VAE for Multi-Phase MRI Synthesis
Separating shared and independent features is crucial for multi-phase contrast-enhanced (CE) MRI synthesis. However, existing methods use deep autoencoder generators with low parameter efficiency and lack interpretable training strategies. In this paper, we propose Flip Distribution Alignment Variational Autoencoder (FDA-VAE), a lightweight feature-decoupled VAE model for multi-phase CE MRI synthesis. Our method encodes input and target images into two latent distributions that are symmetric concerning a standard normal distribution, effectively separating shared and independent features. The Y-shaped bidirectional training strategy further enhances the interpretability of feature separation. Experimental results show that compared to existing deep autoencoder-based end-to-end synthesis methods, FDA-VAE significantly reduces model parameters and inference time while effectively improving synthesis quality. The source code is publicly available at https://github.com/QianMuXiao/FDA-VAE.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
FlipNeRF: Flipped Reflection Rays for Few-shot Novel View Synthesis
Neural Radiance Field (NeRF) has been a mainstream in novel view synthesis with its remarkable quality of rendered images and simple architecture. Although NeRF has been developed in various directions improving continuo…
3D geometryDepth EstimationNeRFNovel View SynthesisWhen Human Preferences Flip: An Instance-Dependent Robust Loss for RLHF
Quality of datasets plays an important role in large language model (LLM) alignment. In collecting human feedback, however, preference flipping is ubiquitous and causes corruption in data annotation; the issue necessitat…
Reinforcement LearningUnlearnable phases of matter
We identify fundamental limitations in machine learning by demonstrating that non-trivial mixed-state phases of matter are computationally hard to learn. Focusing on unsupervised learning of distributions, we show that a…
DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds
Learning the spatial-temporal structure of body movements is a fundamental problem for character motion synthesis. In this work, we propose a novel neural network architecture called the Periodic Autoencoder that can lea…
Motion SynthesisDCGANs for Realistic Breast Mass Augmentation in X-ray Mammography
Early detection of breast cancer has a major contribution to curability, and using mammographic images, this can be achieved non-invasively. Supervised deep learning, the dominant CADe tool currently, has played a great …
Lesion Detectionobject-detectionObject Detection