Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-06-014 ISMRM Abstract

Conditional Diffusion Model for Synthetic Breast Implant Images: Impact on Classification Performance

Primary: Analysis Methods - Generative Models
Secondary: Body - Breast
364-06-014 · Classification in Diffusion MRI II · Monday, 11 May, 5:05 PM–6:00 PM · Digital Posters Row E
Keywords: Synthetic data Breast implant Conditional Diffusion model Unconditional diffusion model
Accepted
Vanika Singhal 1, Harsh Suthar1, Sandeep S Kaushik2, Sajith Rajamani1, Uday Patil1, Pavan Annangi1, Chitresh Bhushan2,3,4, Dattesh Dayanand Shanbhag1
1GE HealthCare, Bengaluru, India
2GE HealthCare, San Ramon, United States of America
3Technology and Innovation Center, GE HealthCare, San Ramon, United States of America
4GE HealthCare., NY, United States of America
Presenting Author: Vanika Singhal

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Saharia, Chitwan, et al. "Image super-resolution via iterative refinement." IEEE transactions on pattern analysis and machine intelligence 45.4 (2022): 4713-4726.
2. Koonce, Brett. "SqueezeNet." Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization. Berkeley, CA: Apress, 2021. 73-85.
3. Ho, Jonathan, Ajay Jain, and Pieter Abbeel. “Denoising Diffusion Probabilistic Models.” Advances in Neural Information Processing Systems, vol. 33, 2020.
4. Oquab, Maxime, et al. DINOv2: Learning Robust Visual Features without Supervision. arXiv, 2 Feb. 2024, arxiv.org/abs/2304.07193.
5. Newitt, D. C., Partridge, S. C., Zhang, Z., Gibbs, J., Chenevert, T., Rosen, M., Bolan, P., Marques, H., Romanoff, J., Cimino, L., Joe, B. N., Umphrey, H., Ojeda-Fournier, H., Dogan, B., Oh, K. Y., Abe, H., Drukteinis, J., Esserman, L. J., & Hylton, N. M. (2021). ACRIN 6698/I-SPY2 Breast DWI [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.KK02-6D95. [doi]
6. David Newitt, Nola Hylton, on behalf of the I-SPY 1 Network and ACRIN 6657 Trial Team. (2016). Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.HdHpgJLK [doi]
7. Lingle, W., Erickson, B. J., Zuley, M. L., Jarosz, R., Bonaccio, E., Filippini, J., Net, J. M., Levi, L., Morris, E. A., Figler, G. G., Elnajjar, P., Kirk, S., Lee, Y., Giger, M., & Gruszauskas, N. (2016). The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA) (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.AB2NAZRP [doi]
8. McInnes, Leland, John Healy, and James Melville. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv, 9 Feb. 2018, arxiv.org/abs/1802.03426.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-06-014