Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation (2405.16850v1)

Published 27 May 2024 in eess.IV, cs.CV, and cs.LG

Abstract: In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are constrained by a one-to-one fitting approach that results in lengthy encoding times. Our novel method, ``\textbf{UniCompress}'', innovatively extends the compression capabilities of INR by being the first to compress multiple medical data blocks using a single INR network. By employing wavelet transforms and quantization, we introduce a codebook containing frequency domain information as a prior input to the INR network. This enhances the representational power of INR and provides distinctive conditioning for different image blocks. Furthermore, our research introduces a new technique for the knowledge distillation of implicit representations, simplifying complex model knowledge into more manageable formats to improve compression ratios. Extensive testing on CT and electron microscopy (EM) datasets has demonstrated that UniCompress outperforms traditional INR methods and commercial compression solutions like HEVC, especially in complex and high compression scenarios. Notably, compared to existing INR techniques, UniCompress achieves a 4$\sim$5 times increase in compression speed, marking a significant advancement in the field of medical image compression. Codes will be publicly available.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. Scale-space flow for end-to-end optimized video compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8503–8512, 2020.
  2. Jpeg xl next-generation image compression architecture and coding tools. In Applications of Digital Image Processing XLII, volume 11137, pages 112–124. SPIE, 2019.
  3. The medical segmentation decathlon. Nature communications, 13(1):4128, 2022.
  4. Wavelet based volumetric medical image compression. Signal processing: Image communication, 31:112–133, 2015.
  5. Exploring the performance of implicit neural representations for brain image registration. Scientific Reports, 13(1):17334, 2023.
  6. Attri-vae: Attribute-based interpretable representations of medical images with variational autoencoders. Computerized Medical Imaging and Graphics, 104:102158, 2023.
  7. Nerv: Neural representations for videos. Advances in Neural Information Processing Systems, 34:21557–21568, 2021.
  8. Learning multiscale consistency for self-supervised electron microscopy instance segmentation. In IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
  9. Self-supervised neuron segmentation with multi-agent reinforcement learning. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pages 609–617, 2023.
  10. Generative text-guided 3d vision-language pretraining for unified medical image segmentation. arXiv preprint arXiv:2306.04811, 2023.
  11. Bimcv-r: A landmark dataset for 3d ct text-image retrieval. arXiv preprint arXiv:2403.15992, 2024.
  12. Qarv: Quantization-aware resnet vae for lossy image compression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
  13. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873–12883, 2021.
  14. Compression with bayesian implicit neural representations. arXiv preprint arXiv:2305.19185, 2023.
  15. Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete drosophila brain. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11, pages 317–325. Springer, 2018.
  16. Incode: Implicit neural conditioning with prior knowledge embeddings. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1298–1307, 2024.
  17. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240, 2020.
  18. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023.
  19. Multi-level wavelet-cnn for image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 773–782, 2018.
  20. Dvc: An end-to-end deep video compression framework. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11006–11015, 2019.
  21. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7210–7219, 2021.
  22. Password complexity prediction based on roberta algorithm. Applied Science and Engineering Journal for Advanced Research, 3(3):1–5, 2024.
  23. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10318–10327, 2021.
  24. Wire: Wavelet implicit neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18507–18516, 2023.
  25. Harnessing xgboost for robust biomarker selection of obsessive-compulsive disorder (ocd) from adolescent brain cognitive development (abcd) data.
  26. Implicit neural representations with periodic activation functions. Advances in neural information processing systems, 33:7462–7473, 2020.
  27. Scene representation networks: Continuous 3d-structure-aware neural scene representations. Advances in Neural Information Processing Systems, 32, 2019.
  28. Overview of the high efficiency video coding (hevc) standard. IEEE Transactions on circuits and systems for video technology, 22(12):1649–1668, 2012.
  29. Low-rank matrix recovery with unknown correspondence. In Robin J. Evans and Ilya Shpitser, editors, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, volume 216 of Proceedings of Machine Learning Research, pages 2111–2122. PMLR, 31 Jul–04 Aug 2023.
  30. Zeroth-order optimization meets human feedback: Provable learning via ranking oracles. In The Twelfth International Conference on Learning Representations, 2024.
  31. z-signfedavg: A unified stochastic sign-based compression for federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 15301–15309, 2024.
  32. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017.
  33. Wavelet filter evaluation for image compression. IEEE Transactions on image processing, 4(8):1053–1060, 1995.
  34. Gregory K Wallace. The jpeg still picture compression standard. Communications of the ACM, 34(4):30–44, 1991.
  35. Private model compression via knowledge distillation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 1190–1197, 2019.
  36. Overview of the h. 264/avc video coding standard. IEEE Transactions on circuits and systems for video technology, 13(7):560–576, 2003.
  37. Dbvc: An end-to-end 3-d deep biomedical video coding framework. IEEE Transactions on Circuits and Systems for Video Technology, 2023.
  38. Runzhao Yang. Tinc: Tree-structured implicit neural compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18517–18526, 2023.
  39. Sci: A spectrum concentrated implicit neural compression for biomedical data. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 4774–4782, 2023.
  40. Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function. bioRxiv, pages 2022–12, 2022.
  41. Deepgi: An automated approach for gastrointestinal tract segmentation in mri scans. arXiv preprint arXiv:2401.15354, 2024.
  42. Generator versus segmentor: Pseudo-healthy synthesis. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24, pages 150–160. Springer, 2021.
  43. Xnet: Wavelet-based low and high frequency fusion networks for fully-and semi-supervised semantic segmentation of biomedical images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 21085–21096, 2023.
  44. Optical flow regularization of implicit neural representations for video frame interpolation. APSIPA Transactions on Signal and Information Processing, 12(1), 2022.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets