Difference between revisions of "Feature-wise normalization for 3D medical images"
From ISLAB/CAISR
| (2 intermediate revisions by one user not shown) | |||
| Line 1: | Line 1: | ||
{{StudentProjectTemplate | {{StudentProjectTemplate | ||
| − | |Summary=Normalization of 3D medical imaging either as a data | + | |Summary=Normalization of 3D medical imaging either as a data pre-processing or as feature-wise batch normalization during CNN model training |
|Keywords=CNN, 3D models | |Keywords=CNN, 3D models | ||
| − | |||
| − | |||
| − | |||
|Supervisor=Amira Soliman, Stefan Byttner, Kobra Etminani | |Supervisor=Amira Soliman, Stefan Byttner, Kobra Etminani | ||
|Level=Master | |Level=Master | ||
Latest revision as of 14:40, 29 September 2020
| Title | Feature-wise normalization for 3D medical images |
|---|---|
| Summary | Normalization of 3D medical imaging either as a data pre-processing or as feature-wise batch normalization during CNN model training |
| Keywords | CNN, 3D models |
| TimeFrame | |
| References | |
| Prerequisites | |
| Author | |
| Supervisor | Amira Soliman, Stefan Byttner, Kobra Etminani |
| Level | Master |
| Status | Open |
Normalization is a required preprocessing step, especially for deep learning and convolutional neural networks, such that the network becomes unbiased towards the different features. However, in medical images, the whole intensity normalization may lead to reduced sensitivity for relatively important features. The objective of this master thesis is to study the state-of-the-art normalization techniques used in 2D images, investigate the applicability of such techniques in 3D medical images, and apply them either as a preprocessing step or as feature-wise batch normalization during the model training.
