3 research outputs found
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
Pose estimation, i.e. predicting a 3D rigid transformation with respect to a
fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image
analysis with applications such as: image rigid registration, anatomical
standard plane detection, tracking and device/camera pose estimation. Deep
learning methods often parameterise a pose with a representation that separates
rotation and translation. As commonly available frameworks do not provide means
to calculate loss on a manifold, regression is usually performed using the
L2-norm independently on the rotation's and the translation's
parameterisations, which is a metric for linear spaces that does not take into
account the Lie group structure of SE(3). In this paper, we propose a general
Riemannian formulation of the pose estimation problem. We propose to train the
CNN directly on SE(3) equipped with a left-invariant Riemannian metric,
coupling the prediction of the translation and rotation defining the pose. At
each training step, the ground truth and predicted pose are elements of the
manifold, where the loss is calculated as the Riemannian geodesic distance. We
then compute the optimisation direction by back-propagating the gradient with
respect to the predicted pose on the tangent space of the manifold SE(3) and
update the network weights. We thoroughly evaluate the effectiveness of our
loss function by comparing its performance with popular and most commonly used
existing methods, on tasks such as image-based localisation and intensity-based
2D/3D registration. We also show that hyper-parameters, used in our loss
function to weight the contribution between rotations and translations, can be
intrinsically calculated from the dataset to achieve greater performance
margins
Medical Image Registration Using Deep Neural Networks: A Comprehensive Review
Image-guided interventions are saving the lives of a large number of patients
where the image registration problem should indeed be considered as the most
complex and complicated issue to be tackled. On the other hand, the recently
huge progress in the field of machine learning made by the possibility of
implementing deep neural networks on the contemporary many-core GPUs opened up
a promising window to challenge with many medical applications, where the
registration is not an exception. In this paper, a comprehensive review on the
state-of-the-art literature known as medical image registration using deep
neural networks is presented. The review is systematic and encompasses all the
related works previously published in the field. Key concepts, statistical
analysis from different points of view, confiding challenges, novelties and
main contributions, key-enabling techniques, future directions and prospective
trends all are discussed and surveyed in details in this comprehensive review.
This review allows a deep understanding and insight for the readers active in
the field who are investigating the state-of-the-art and seeking to contribute
the future literature.Comment: 45 Pages, 39 Figures, 10 Tables, 2 Appendixe
A Comparative Analysis of Machine Learning and Grey Models
Artificial Intelligence (AI) has recently shown its capabilities for almost
every field of life. Machine Learning, which is a subset of AI, is a `HOT'
topic for researchers. Machine Learning outperforms other classical forecasting
techniques in almost all-natural applications. It is a crucial part of modern
research. As per this statement, Modern Machine Learning algorithms are hungry
for big data. Due to the small datasets, the researchers may not prefer to use
Machine Learning algorithms. To tackle this issue, the main purpose of this
survey is to illustrate, demonstrate related studies for significance of a
semi-parametric Machine Learning framework called Grey Machine Learning (GML).
This kind of framework is capable of handling large datasets as well as small
datasets for time series forecasting likely outcomes. This survey presents a
comprehensive overview of the existing semi-parametric machine learning
techniques for time series forecasting. In this paper, a primer survey on the
GML framework is provided for researchers. To allow an in-depth understanding
for the readers, a brief description of Machine Learning, as well as various
forms of conventional grey forecasting models are discussed. Moreover, a brief
description on the importance of GML framework is presented.Comment: 22 pages, 8 figures, journal pape