Feature-wise normalization for 3D medical images

Title	Feature-wise normalization for 3D medical images
Summary	Normalization of 3D medical imaging either as a data reprocessing or as feature-wise batch normalization during CNN model training
Keywords	CNN, 3D models
TimeFrame	2020 Fall - 2021 Summer
References
Prerequisites	Excellent Programming Skills Excellent knowledge in Machine Learning and Neural Networks
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.