Medical Image Learning with Limited and Noisy Data
Medical Image Learning with Limited and Noisy Data [electronic resource] :
edited by Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang.
- 1st ed. 2023.
- Cham Springer Nature Switzerland 2023
- XI, 270 p. 77 illus., 72 illus. in color. online resource.
This book consists of full papers presented in the 2nd workshop of "Medical Image Learning with Noisy and Limited Data (MILLanD)" held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
9783031449178
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer vision.
Image processing
6
This book consists of full papers presented in the 2nd workshop of "Medical Image Learning with Noisy and Limited Data (MILLanD)" held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
9783031449178
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer vision.
Image processing
6