Motion-Guided Causal Disentanglement for Robust Multi-View Cine Cardiac MRI Diagnosis

• URL: http://arxiv.org/abs/2606.04414v1
• Date: Wed, 03 Jun 2026 03:45:11 GMT
• Title: Motion-Guided Causal Disentanglement for Robust Multi-View Cine Cardiac MRI Diagnosis
• Abstract summary: We propose a Motion-Guided View-Disease Disentanglement framework MoViD built upon a ViT-MAE backbone.<n>We evaluate the framework on a private clinical venous thrombosis dataset and two public benchmarks.
• Score: 0.4638331367650009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view cardiac magnetic resonance (CMR) imaging provides complementary anatomical information and is widely used for noninvasive disease assessment. Recent transformer-based models have demonstrated strong representation learning capabilities for CMR analysis; however, they typically learn unified latent embeddings that entangle view-specific anatomical variations with disease-related features. Such entanglement biases classifiers toward structural attributes rather than view-invariant pathological patterns. This issue is exacerbated in low-data regimes, particularly for underrepresented cardiac conditions, where limited samples increase the susceptibility to shortcut learning and view-dependent decision boundaries. To address this, we propose a Motion-Guided View--Disease Disentanglement framework MoViD built upon a ViT-MAE backbone. The model explicitly factorizes latent representations into view-specific and disease-discriminative components using dual-branch supervised contrastive objectives and a gradient-reversal adversarial constraint that minimizes disease leakage into the view embedding. Additionally, an annotation-free temporal motion feature, derived from inter-frame difference maps, is introduced to localize the beating heart region and suppress background artifacts. A focal reweighting mechanism is incorporated into the contrastive loss to mitigate class imbalance. We evaluate the framework on a private clinical venous thrombosis dataset and two public benchmarks (M&Ms, M&Ms2). Across disease classification and cardiac segmentation tasks, our approach consistently outperforms standard transformer baselines and demonstrates competitive performance against large-scale pretrained foundation models, validating the efficacy of structural disentanglement in medical image analysis.

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