Unravelling the effect of data augmentation transformations in polyp segmentation.

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.

Methods

A set of transformations and ranges have been selected, considering image-based (width and height shift, rotation, shear, zooming, horizontal and vertical flip and elastic deformation), pixel-based (changes in brightness and contrast) and application-based (specular lights and blurry frames) transformations. A model has been trained under the same conditions without data augmentation transform

SUBMITTER: Sanchez-Peralta LF 

PROVIDER: S-EPMC7671995 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature