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Classroom Contents
The Role of Data and Models for Deep-Learning Based Image Reconstruction
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- 1 Intro
- 2 Scaling language models
- 3 Data set sizes in imaging tasks are small
- 4 Expected performance behavior
- 5 U-net-based denoising
- 6 U-net-based accelerated MRI
- 7 Swin transformer based denoising
- 8 Reconstruction methods
- 9 What we might expect
- 10 Dataset shift
- 11 Adversarially filtered shift
- 12 Goal: improve performance unter distribution shifts
- 13 For classification problems, "natural distribution shifts are an open research problem"
- 14 Improving performance for 11-minimization is easy
- 15 Test time training
- 16 Closing the distribution shift performance gap for anatomy shift
- 17 Closing the distribution shift performance gap with test-time-training
- 18 References
