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An analysis of the often overlooked effects of different types of data augmentation and preprocessing for medical image segmentation using novel kernel attention u-nets
A Closer Look at Data Augmentation and Preprocessing for Segmentation with Kernel Attention U-Net's
Final Project for MBP1413: Biomedical Applicationg of Artificial Intelligence
Authors
Sayan Nag
Shawn Carere
To train a model and replicate results, use the train_baselines_shawn.py script in the training directory.
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An analysis of the often overlooked effects of different types of data augmentation and preprocessing for medical image segmentation using novel kernel attention u-nets