mlsanityData to 7 Oct 2026

Controllable cardiac synthesis via disentangled anatomy arithmetic

arXiv 2107.01748 ↗First posted 4 Jul 2021eess.IV cs.CVcited by 9

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Abstract (arXiv)

Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can correct such underrepresentation. Assuming a proper latent representation, the idea of a "latent vector arithmetic" could offer the means of achieving such synthesis. A proper representation must encode the fidelity of the input data, preserve invariance and equivariance, and permit arithmetic operations. Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (vector) representations, we propose a framework termed "disentangled anatomy arithmetic", in which a generative model learns to combine anatomical factors of different input images such that when they are re-entangled with the desired imaging modality (e.g. MRI), plausible new cardiac images are created with the target characteristics. To encourage a realistic combination of anatomy factors after the arithmetic step, we propose a localized noise injection network that precedes the generator. Our model is used to generate realistic images, pathology labels, and segmentation masks that are used to augment the existing datasets and subsequently improve post-hoc classification and segmentation tasks. Code is publicly available at https://github.com/vios-s/DAA-GAN.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 25 of its 38 references that are in the map. First posted on arXiv 4 Jul 2021.

Builds on

25 of 38 references.

References · 25

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  5. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  6. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  7. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  8. 2016Least Squares Generative Adversarial Networkscited by 1,000
  9. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  10. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  11. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 146
  12. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 131
13 more
  1. 2016Medical Image Synthesis with Context-Aware Generative Adversarial Networkscited by 91
  2. 2019Disentangled Representation Learning in Cardiac Image Analysiscited by 69
  3. 2020Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusioncited by 67
  4. 2019Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentationcited by 50
  5. 2020Disentangled Image Generation Through Structured Noise Injectioncited by 39
  6. 2018CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentationcited by 34
  7. 2017Synthetic Medical Images from Dual Generative Adversarial Networkscited by 32
  8. 2019Demystifying Inter-Class Disentanglementcited by 31
  9. 2018Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networkscited by 15
  10. 2020Conditional Generation of Medical Images via Disentangled Adversarial Inferencecited by 13
  11. 2018Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translationcited by 10
  12. 2017Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Resultscited by 9
  13. 2018ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networkscited by 4

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Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 1; 2025: 1.

9 papers.

  1. 2021Learning Disentangled Representations in the Imaging Domaincited by 46
  2. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challengescited by 24
  3. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  4. 2023SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defectscited by 17
  5. 2022HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysiscited by 3
  6. 2022Why patient data cannot be easily forgotten?cited by 3
  7. 2025Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?cited by 2
  8. 2022HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Informationcited by 1
  9. 2023Compositional Representation Learning for Brain Tumour Segmentationcited by 0