التفاصيل البيبلوغرافية
| العنوان: |
iUNets: Learnable Invertible Up- and Downsampling for Large-Scale Inverse Problems |
| المؤلفون: |
Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb |
| المصدر: |
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
MLSP |
| سنة النشر: |
2020 |
| مصطلحات موضوعية: |
Artificial neural network, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Inverse problem, 01 natural sciences, Backpropagation, 030218 nuclear medicine & medical imaging, law.invention, 010309 optics, Upsampling, 03 medical and health sciences, 0302 clinical medicine, Invertible matrix, law, 0103 physical sciences, Medical imaging, Segmentation, Algorithm, Block (data storage) |
| الوصف: |
U-Nets have been established as a standard neural network architecture for image-to-image problems such as segmentation and inverse problems in imaging. For high-dimensional applications, as they for example appear in 3D medical imaging, U-Nets however have prohibitive memory requirements. Here, we present a new fully-invertible U-Net-based architecture called the iUNet, which allows for the application of highly memory-efficient backpropagation procedures. As its main building block, we introduce learnable and invertible up- an downsampling operations. For this, we developed an open-source implementation in Pytorch for 1D, 2D and 3D data. |
| ردمك: |
978-1-72816-662-9 |
| DOI: |
10.1109/mlsp49062.2020.9231874 |
| URL الوصول: |
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::890b044c0fc34a3906833e6688ea7c73 |
| Rights: |
CLOSED |
| رقم الانضمام: |
edsair.doi.dedup.....890b044c0fc34a3906833e6688ea7c73 |
| قاعدة البيانات: |
OpenAIRE |