Unravelling the effect of data augmentation transformations in polyp segmentation.
Journal:
International journal of computer assisted radiology and surgery
Published Date:
Dec 1, 2020
Abstract
PURPOSE: Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a particular field. This work aims at identifying the effect of different transformations on polyp segmentation using deep learning.