6,202 research outputs found
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Dense conditional random fields (CRFs) have become a popular framework for
modelling several problems in computer vision such as stereo correspondence and
multi-class semantic segmentation. By modelling long-range interactions, dense
CRFs provide a labelling that captures finer detail than their sparse
counterparts. Currently, the state-of-the-art algorithm performs mean-field
inference using a filter-based method but fails to provide a strong theoretical
guarantee on the quality of the solution. A question naturally arises as to
whether it is possible to obtain a maximum a posteriori (MAP) estimate of a
dense CRF using a principled method. Within this paper, we show that this is
indeed possible. We will show that, by using a filter-based method, continuous
relaxations of the MAP problem can be optimised efficiently using
state-of-the-art algorithms. Specifically, we will solve a quadratic
programming (QP) relaxation using the Frank-Wolfe algorithm and a linear
programming (LP) relaxation by developing a proximal minimisation framework. By
exploiting labelling consistency in the higher-order potentials and utilising
the filter-based method, we are able to formulate the above algorithms such
that each iteration has a complexity linear in the number of classes and random
variables. The presented algorithms can be applied to any labelling problem
using a dense CRF with sparse higher-order potentials. In this paper, we use
semantic segmentation as an example application as it demonstrates the ability
of the algorithm to scale to dense CRFs with large dimensions. We perform
experiments on the Pascal dataset to indicate that the presented algorithms are
able to attain lower energies than the mean-field inference method
Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks
In this paper, we describe how a patient-specific, ultrasound-probe-induced
prostate motion model can be directly generated from a single preoperative MR
image. Our motion model allows for sampling from the conditional distribution
of dense displacement fields, is encoded by a generative neural network
conditioned on a medical image, and accepts random noise as additional input.
The generative network is trained by a minimax optimisation with a second
discriminative neural network, tasked to distinguish generated samples from
training motion data. In this work, we propose that 1) jointly optimising a
third conditioning neural network that pre-processes the input image, can
effectively extract patient-specific features for conditioning; and 2)
combining multiple generative models trained separately with heuristically
pre-disjointed training data sets can adequately mitigate the problem of mode
collapse. Trained with diagnostic T2-weighted MR images from 143 real patients
and 73,216 3D dense displacement fields from finite element simulations of
intraoperative prostate motion due to transrectal ultrasound probe pressure,
the proposed models produced physically-plausible patient-specific motion of
prostate glands. The ability to capture biomechanically simulated motion was
evaluated using two errors representing generalisability and specificity of the
model. The median values, calculated from a 10-fold cross-validation, were
2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced
approach demonstrates the feasibility of applying state-of-the-art machine
learning algorithms to generate organ motion models from patient images, and
shows significant promise for future research.Comment: Accepted to MICCAI 201
Learning Active Learning from Data
In this paper, we suggest a novel data-driven approach to active learning
(AL). The key idea is to train a regressor that predicts the expected error
reduction for a candidate sample in a particular learning state. By formulating
the query selection procedure as a regression problem we are not restricted to
working with existing AL heuristics; instead, we learn strategies based on
experience from previous AL outcomes. We show that a strategy can be learnt
either from simple synthetic 2D datasets or from a subset of domain-specific
data. Our method yields strategies that work well on real data from a wide
range of domains
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
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