Machine Learning provides powerful tools for a variety of applications,
including disease diagnosis through medical image classification. In recent
years, quantum machine learning techniques have been put forward as a way to
potentially enhance performance in machine learning applications, both through
quantum algorithms for linear algebra and quantum neural networks. In this
work, we study two different quantum neural network techniques for medical
image classification: first by employing quantum circuits in training of
classical neural networks, and second, by designing and training quantum
orthogonal neural networks. We benchmark our techniques on two different
imaging modalities, retinal color fundus images and chest X-rays. The results
show the promises of such techniques and the limitations of current quantum
hardware.
Beschreibung
Medical image classification via quantum neural networks
%0 Generic
%1 mathur2021medical
%A Mathur, Natansh
%A Landman, Jonas
%A Li, Yun Yvonna
%A Strahm, Martin
%A Kazdaghli, Skander
%A Prakash, Anupam
%A Kerenidis, Iordanis
%D 2021
%K machinelearn
%T Medical image classification via quantum neural networks
%U http://arxiv.org/abs/2109.01831
%X Machine Learning provides powerful tools for a variety of applications,
including disease diagnosis through medical image classification. In recent
years, quantum machine learning techniques have been put forward as a way to
potentially enhance performance in machine learning applications, both through
quantum algorithms for linear algebra and quantum neural networks. In this
work, we study two different quantum neural network techniques for medical
image classification: first by employing quantum circuits in training of
classical neural networks, and second, by designing and training quantum
orthogonal neural networks. We benchmark our techniques on two different
imaging modalities, retinal color fundus images and chest X-rays. The results
show the promises of such techniques and the limitations of current quantum
hardware.
@misc{mathur2021medical,
abstract = {Machine Learning provides powerful tools for a variety of applications,
including disease diagnosis through medical image classification. In recent
years, quantum machine learning techniques have been put forward as a way to
potentially enhance performance in machine learning applications, both through
quantum algorithms for linear algebra and quantum neural networks. In this
work, we study two different quantum neural network techniques for medical
image classification: first by employing quantum circuits in training of
classical neural networks, and second, by designing and training quantum
orthogonal neural networks. We benchmark our techniques on two different
imaging modalities, retinal color fundus images and chest X-rays. The results
show the promises of such techniques and the limitations of current quantum
hardware.},
added-at = {2023-05-10T22:58:59.000+0200},
author = {Mathur, Natansh and Landman, Jonas and Li, Yun Yvonna and Strahm, Martin and Kazdaghli, Skander and Prakash, Anupam and Kerenidis, Iordanis},
biburl = {https://www.bibsonomy.org/bibtex/27b679ef2a603c376dced5fb67fdb45f2/cmcneile},
description = {Medical image classification via quantum neural networks},
interhash = {4b86b3f0d526f1e701aaf9a88046921c},
intrahash = {7b679ef2a603c376dced5fb67fdb45f2},
keywords = {machinelearn},
note = {cite arxiv:2109.01831},
timestamp = {2023-05-10T22:58:59.000+0200},
title = {Medical image classification via quantum neural networks},
url = {http://arxiv.org/abs/2109.01831},
year = 2021
}