Score-based diffusion models for accelerated MRI.pptx
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Unsupervised deep learning methods for biological image reconstruction and enhancement
- 1. Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement Mehmet Akckaya, Burhaneddin Yaman, Hyungjin Chung, Jong Chul Ye Hyungjin Chung
- 2. Introduction
- 3. Introduction: Inverse Problems Inverse problems 𝒚 = 𝐻 𝒙 + 𝒘 Regularized Least Squares (RLS) 𝑥 = arg min 𝒙 ‖ ‖ 𝒚 − 𝐻 𝒙 2 2 + 𝑅(𝒙)
- 4. Self-supervised learning based methods
- 5. Self-supervised learning based methods Batson et al. 2019 Denoising min 𝜽𝑑 𝑛=1 𝑁 𝐹𝜽𝑑 𝒚𝑛 − 𝒚𝑛 2
- 6. Self-supervised learning based methods Reconstruction min 𝜽𝑟 1 𝑁 𝑛=1 𝑁 ℒ(𝒚Θc 𝑛 , 𝐻Θc 𝑛 (𝐹𝜽𝑟 𝒚Θ 𝑛 , 𝐻Θ 𝑛 )) Yaman et al. 2020
- 7. Self-supervised learning based methods: Results Denoising Yaman et al. 2020 Batson et al. 2019 Reconstruction
- 8. Generative model based methods
- 9. Generative model based methods Geometry of generative models
- 10. Generative model based methods: VAE • Vanilla VAE (Kingma et al. 2014) • Spatial-VAE (Bepler et al. 2019) • DIVNOISING (Prakash et al. 2021)
- 11. Generative model based methods: GAN
- 12. Generative model based methods: cycleGAN Geometry of cycleGAN
- 13. Generative model based methods: Results ODT reconstruction Chung et al. 2021
