mlsanityData to 14 Oct 2026

H-LDM: Hierarchical Latent Diffusion Models for Controllable and Interpretable PCG Synthesis from Clinical Metadata

arXiv 2511.14312 ↗First posted 18 Nov 2025cs.LG cs.AIcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Phonocardiogram (PCG) analysis is vital for cardiovascular disease diagnosis, yet the scarcity of labeled pathological data hinders the capability of AI systems. To bridge this, we introduce H-LDM, a Hierarchical Latent Diffusion Model for generating clinically accurate and controllable PCG signals from structured metadata. Our approach features: (1) a multi-scale VAE that learns a physiologically-disentangled latent space, separating rhythm, heart sounds, and murmurs; (2) a hierarchical text-to-biosignal pipeline that leverages rich clinical metadata for fine-grained control over 17 distinct conditions; and (3) an interpretable diffusion process guided by a novel Medical Attention module. Experiments on the PhysioNet CirCor dataset demonstrate state-of-the-art performance, achieving a Fr'echet Audio Distance of 9.7, a 92% attribute disentanglement score, and 87.1% clinical validity confirmed by cardiologists. Augmenting diagnostic models with our synthetic data improves the accuracy of rare disease classification by 11.3%. H-LDM establishes a new direction for data augmentation in cardiac diagnostics, bridging data scarcity with interpretable clinical insights.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 15 of its 22 references that are in the map. First posted on arXiv 18 Nov 2025.

Builds on

15 of 22 references.

References · 15

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,354
  5. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  6. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 622
  7. 2023AudioLDM: Text-to-Audio Generation with Latent Diffusion Modelscited by 599
  8. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 553
  9. 2023NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Modelscited by 76
  10. 2025MegaTTS 3: Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesiscited by 41
  11. 2021The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classificationcited by 25
  12. 2023Hierarchical Diffusion Autoencoders and Disentangled Image Manipulationcited by 18
3 more
  1. 2024Nested Diffusion Models Using Hierarchical Latent Priorscited by 2
  2. 2024LatentSpeech: Latent Diffusion for Text-To-Speech Generationcited by 2
  3. 2025Hyper-Transforming Latent Diffusion Modelscited by 2

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  1. 2026Diffusion-Based Heart Sound Generation: Evaluation with Physiological Signal Metrics, Classifiers, and Expert Listening ↗
  2. 2025Phenotype-Guided Generative Model for High-Fidelity Cardiac MRI Synthesis: Advancing Pretraining and Clinical Applications
  3. 2024HeartBeat: Towards Controllable Echocardiography Video Synthesis with Multimodal Conditions-Guided Diffusion Models
  4. 2025MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials
  5. 2024Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms
  6. 2023Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
  7. 2023DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis
  8. 2026Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis