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Abstract

The development of robust machine learning models for medical diagnostics is constrained by the scarcity of large, annotated imaging datasets, a challenge rooted in high acquisition costs and stringent privacy regulations. To address this, generative approaches for data synthesis have emerged as an essential tool, and this study explores the use of diffusion models to generate medical images. This study introduces a novel Diffusion Autoencoder Model (DAM) integrated with a semantic encoder framework to generate interpretable and decodable image representations. Our DAM learns disentangled latent features via its encoder, which then conditions a diffusion probabilistic model acting as the decoder, enabling the synthesis of highly informative medical images. The model’s performance is assessed by using quantitative metrics demonstrating superior results compared to state-of-the-art generative models. In addition to its generative precision, the proposed model has significant potential for image classification. Notably, the semantic encoder learned by the model generates representations that lead to a significant increase in classification accuracy: for PneumoniaMNIST, the binary classification accuracy improved from 0.854 to 0.95; for RetinaMNIST, the multiclass accuracy increased from 0.528 to 0.94. These findings underscore the potential of DAMs to tackle data scarcity issues, enhance classification performance, and ultimately improve diagnostic methodologies.

References

  1. P. Whig, B. Yadav Kasula, N. Yathiraju, A. Jain, S. Sharma, Transforming Gender-Based Healthcare with AI and Machine Learning, CRC Press, USA, 2025, p.14.
  2. A. Nazir, A. Hussain, A. Singh, A. Assad, Multimed. Tools Appl. 84 (2025) 17677-1.
  3. O. Panahi, J. Med. Discov. 2/1 (2025) 6.
  4. T. Davenport, R. Kalakota, Future Healthc. J. 6/2 (2019) 94.
  5. S. Kumar, Int. J. Sci. Res. Manag. 13/4 (2025) 2136.
  6. S. Akbarian, L. Seyyed-kalantari, F. Khalvati, E. Dolatabadi, IEEE Access. 11 (2023) 85812.
  7. O. Shobayo, R. Saatchi, Diagnostics. 15/9 (2025) 1072.
  8. R. Nosowsky, T.J. Giordano, Annu. Rev. Med. 57 (2006) 575.
  9. A. Sylolypavan, D. Sleeman, H. Wu, M. Sim, NPJ Digit. Med. 6 (2023) 26.
  10. A. Bansal, R. Sharma, M. Kathuria, ACM Comput. Surv. 54/10s (2022) 29.
  11. A. DuMont Schütte, J. Hetzel, S. Gatidis, T. Hepp, et al., NPJ Digit. Med. 4 (2021) 141.
  12. M. Ali, M. Ali, M. Hussain, D. Koundal, Arch. Comput. Methods Eng. 32/2 (2025) 1185.
  13. Y. Liu, U.R. Acharya, J.H. Tan, Comput. Methods Programs Biomed. 260 (2025) 108571.
  14. O. Oribjonov, Mod. Sci. Res. 4 (2025) 499.
  15. S. Sajed, A. Sanati, J.E. Garcia, H. Rostami, K. Keshavarz, A. Teixeira, Appl. Soft Comput. 147 (2023) 110817.
  16. Y. Fujinami-Yokokawa, H. Ninomiya, X. Liu, L. Yang, N. Pontikos, K. Yoshitake, et al., Br. J. Ophthalmol. 105 (2021) 1272.
  17. L. Seyyed-Kalantari, H. Zhang, M.B.A. McDermott, I.Y. Chen, M. Ghassemi, Nat. Med. 27 (2021) 2176.
  18. V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D. Wu, A.C. Narayanaswamy, S.Venugopalan, JAMA. 316/22 (2016) 2402.
  19. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., Commun. ACM. 63 (2020) 139.
  20. D.P. Kingma, M. Welling, arXiv. (2013) 1312.6114.
  21. M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, H. Greenspan, Neurocomputing. 321 (2018) 321.
  22. K. Rais, M. Amroune, M.Y. Haouam, 2024 6th International Conference on Pattern Analysis and Intelligent Systems, Algeria, 2024, p.8.
  23. Z. Ding, S. Jiang, J. Zhao, 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information, China, 2022, p.597.
  24. R. Wei, C. Garcia, A. El-Sayed, V. Peterson, A. Mahmood, IEEE Access. 8 (2020) 153651.
  25. J. Ho, A. Jain, P. Abbeel, Adv. Neural Inf. Process. Syst. 33 (2020) 6840
  26. P. Dhariwal, A. Nichol, Adv. Neural Inf. Process. Syst. 34 (2021) 8780.
  27. L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, et al,, ACM Comput. Surv. 56 (2023) 39.
  28. H. Chen, Q. Xiang, J. Hu, M. Ye, C. Yu, H. Cheng, et al., Artif. Intell. Rev. 58/4 (2025) 99.
  29. A. Q. Nichol, P. Dhariwal, Proc. 38th Int. Conf. Mach. Learn. 139 (2021) 8162.
  30. H. Li, Y. Yang, M. Chang, S. Chen, H. Feng, Z. Xu, et al., Neurocomputing. 479 (2022) 47.
  31. A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, L. Van Gool, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, USA, 2022, p.11451.
  32. T. Amit, T. Shaharbany, E. Nachmani, L. Wolf, arXiv. (2021) 2112.00390.
  33. X. Han, H. Zheng, M. Zhou, Adv. Neural Inf. Process. Syst. 35 (2022) 18100.
  34. J. Song, C. Meng, S. Ermon, arXiv. (2020) 2010.02502.
  35. Y. Song, J. Sohl-Dickstein, D.P. Kingma, A. Kumar, S. Ermon, B. Poole, arXiv. (2020) 2011.1345
  36. Y. Song, S. Ermon, Adv. Neural Inf. Process. Syst. 33 (2020) 12438.
  37. Y. Song. S. Ermon, Adv. Neural Inf. Process. Syst. 32 (2019).
  38. A. Vahdat, K. Kreis, J. Kautz, Adv. Neural Inf. Process. Syst. 34 (2021) 11287.
  39. K. Preechakul, N. Chatthee, S. Wizadwongsa S. Suwajanakorn, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, USA, 2022, p.10609.
  40. J. Chen, H. Cai, J. Chen, E. Xie, S. Yang, H. Tang, et al., arXiv. (2024) 2410.10733.
  41. A. Komanduri, C. Zhao, F. Chen, X. Wu, arXiv. (2024) 2404.17735.
  42. L. Li, K. Fan, B. Gong, X. Yue, arXiv (2024) 2411.17784.
  43. K. He, X. Zhang, S. Ren, J. Sun, 2016 IEEE Conference on Computer Vision and Pattern Recognition, USA, 2016, p.770,
  44. O. Ronneberger, P. Fischer, Thomas Brox, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, Lecture Notes in Computer Science, vol 9351, Springer, Cham, 2015.
  45. H. Zheng, J. Fu, Y. Zeng, J. Luo, Z.-J. Zha, Adv. Neural Inf. Process. Syst. 33 (2020) 21853.
  46. J. Yang, R. Shi, D. Wei, Z. Liu, L. Zhao, B. Ke, et al., Sci. Data. 10 (2023) 41.
  47. J. Yang, R. Shi, B. Ni, 2021 IEEE 18th International Symposium on Biomedical Imaging, France, 2021, p.191.
  48. R. Liu, X. Wang, Q. Wu, L. Dai, X. Fang, T. Yan, et al., Patterns. 3/6 (2022) 100512.
  49. X. Mao, Q. Li, H. Xie, R.Y.K. Lau, Z. Wang, S.P. Smolley, 2017 IEEE International Conference on Computer Vision, Italy, 2017, p.2813
  50. V.M. Panaretos, Y. Zemel, Annu. Rev. Stat. Appl. 6 (2019) 405

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