Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development
Egileak: Blanca Zufiria Gerbolés Kristin May Rebescher Iván Lalaguna Esther Albertín Marcía B. Cimadevila Javier García María J. Ledesma
Data: 18.09.2022
Abstract
The development of democratized, generalizable deep learning applications for health care systems is challenging as potential biases could easily emerge. This paper provides an overview of the potential biases that appear in image analysis datasets that affect the development and performance of artificial intelligence algorithms. Especially, an exhaustive analysis of mammography data has been carried out at the patient, image and source of origin levels. Furthermore, we summarize some techniques to alleviate these biases for the development of fair deep learning models. We present a learning task to classify negative and positive screening mammographies and analyze the influence of biases in the performance of the algorithm.
BIB_text
title = {Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development},
pages = {59-67},
keywds = {
bias, deep learning, mammography, breast cancer
}
abstract = {
The development of democratized, generalizable deep learning applications for health care systems is challenging as potential biases could easily emerge. This paper provides an overview of the potential biases that appear in image analysis datasets that affect the development and performance of artificial intelligence algorithms. Especially, an exhaustive analysis of mammography data has been carried out at the patient, image and source of origin levels. Furthermore, we summarize some techniques to alleviate these biases for the development of fair deep learning models. We present a learning task to classify negative and positive screening mammographies and analyze the influence of biases in the performance of the algorithm.
}
isbn = {978-303117720-0},
date = {2022-09-18},
}
