238 research outputs found
MR image reconstruction using deep density priors
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled
measurements exploit prior information to compensate for missing k-space data.
Deep learning (DL) provides a powerful framework for extracting such
information from existing image datasets, through learning, and then using it
for reconstruction. Leveraging this, recent methods employed DL to learn
mappings from undersampled to fully sampled images using paired datasets,
including undersampled and corresponding fully sampled images, integrating
prior knowledge implicitly. In this article, we propose an alternative approach
that learns the probability distribution of fully sampled MR images using
unsupervised DL, specifically Variational Autoencoders (VAE), and use this as
an explicit prior term in reconstruction, completely decoupling the encoding
operation from the prior. The resulting reconstruction algorithm enjoys a
powerful image prior to compensate for missing k-space data without requiring
paired datasets for training nor being prone to associated sensitivities, such
as deviations in undersampling patterns used in training and test time or coil
settings. We evaluated the proposed method with T1 weighted images from a
publicly available dataset, multi-coil complex images acquired from healthy
volunteers (N=8) and images with white matter lesions. The proposed algorithm,
using the VAE prior, produced visually high quality reconstructions and
achieved low RMSE values, outperforming most of the alternative methods on the
same dataset. On multi-coil complex data, the algorithm yielded accurate
magnitude and phase reconstruction results. In the experiments on images with
white matter lesions, the method faithfully reconstructed the lesions.
Keywords: Reconstruction, MRI, prior probability, machine learning, deep
learning, unsupervised learning, density estimationComment: Published in IEEE TMI. Main text and supplementary material, 19 pages
tota
Visual Feature Attribution using Wasserstein GANs
Attributing the pixels of an input image to a certain category is an
important and well-studied problem in computer vision, with applications
ranging from weakly supervised localisation to understanding hidden effects in
the data. In recent years, approaches based on interpreting a previously
trained neural network classifier have become the de facto state-of-the-art and
are commonly used on medical as well as natural image datasets. In this paper,
we discuss a limitation of these approaches which may lead to only a subset of
the category specific features being detected. To address this problem we
develop a novel feature attribution technique based on Wasserstein Generative
Adversarial Networks (WGAN), which does not suffer from this limitation. We
show that our proposed method performs substantially better than the
state-of-the-art for visual attribution on a synthetic dataset and on real 3D
neuroimaging data from patients with mild cognitive impairment (MCI) and
Alzheimer's disease (AD). For AD patients the method produces compellingly
realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
While deep neural network models offer unmatched classification performance,
they are prone to learning spurious correlations in the data. Such dependencies
on confounding information can be difficult to detect using performance metrics
if the test data comes from the same distribution as the training data.
Interpretable ML methods such as post-hoc explanations or inherently
interpretable classifiers promise to identify faulty model reasoning. However,
there is mixed evidence whether many of these techniques are actually able to
do so. In this paper, we propose a rigorous evaluation strategy to assess an
explanation technique's ability to correctly identify spurious correlations.
Using this strategy, we evaluate five post-hoc explanation techniques and one
inherently interpretable method for their ability to detect three types of
artificially added confounders in a chest x-ray diagnosis task. We find that
the post-hoc technique SHAP, as well as the inherently interpretable Attri-Net
provide the best performance and can be used to reliably identify faulty model
behavior
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Identifying and interpreting fetal standard scan planes during 2D ultrasound
mid-pregnancy examinations are highly complex tasks which require years of
training. Apart from guiding the probe to the correct location, it can be
equally difficult for a non-expert to identify relevant structures within the
image. Automatic image processing can provide tools to help experienced as well
as inexperienced operators with these tasks. In this paper, we propose a novel
method based on convolutional neural networks which can automatically detect 13
fetal standard views in freehand 2D ultrasound data as well as provide a
localisation of the fetal structures via a bounding box. An important
contribution is that the network learns to localise the target anatomy using
weak supervision based on image-level labels only. The network architecture is
designed to operate in real-time while providing optimal output for the
localisation task. We present results for real-time annotation, retrospective
frame retrieval from saved videos, and localisation on a very large and
challenging dataset consisting of images and video recordings of full clinical
anomaly screenings. We found that the proposed method achieved an average
F1-score of 0.798 in a realistic classification experiment modelling real-time
detection, and obtained a 90.09% accuracy for retrospective frame retrieval.
Moreover, an accuracy of 77.8% was achieved on the localisation task.Comment: 12 pages, 8 figures, published in IEEE Transactions in Medical
Imagin
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Multi-atlas segmentation is a widely used tool in medical image analysis,
providing robust and accurate results by learning from annotated atlas
datasets. However, the availability of fully annotated atlas images for
training is limited due to the time required for the labelling task.
Segmentation methods requiring only a proportion of each atlas image to be
labelled could therefore reduce the workload on expert raters tasked with
annotating atlas images. To address this issue, we first re-examine the
labelling problem common in many existing approaches and formulate its solution
in terms of a Markov Random Field energy minimisation problem on a graph
connecting atlases and the target image. This provides a unifying framework for
multi-atlas segmentation. We then show how modifications in the graph
configuration of the proposed framework enable the use of partially annotated
atlas images and investigate different partial annotation strategies. The
proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets
for hippocampal and cardiac segmentation. Experiments were performed aimed at
(1) recreating existing segmentation techniques with the proposed framework and
(2) demonstrating the potential of employing sparsely annotated atlas data for
multi-atlas segmentation
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
Interpretability is essential for machine learning algorithms in high-stakes
application fields such as medical image analysis. However, high-performing
black-box neural networks do not provide explanations for their predictions,
which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc
explanation techniques, which are widely used in practice, have been shown to
suffer from severe conceptual problems. Furthermore, as we show in this paper,
current explanation techniques do not perform adequately in the multi-label
scenario, in which multiple medical findings may co-occur in a single image. We
propose Attri-Net, an inherently interpretable model for multi-label
classification. Attri-Net is a powerful classifier that provides transparent,
trustworthy, and human-understandable explanations. The model first generates
class-specific attribution maps based on counterfactuals to identify which
image regions correspond to certain medical findings. Then a simple logistic
regression classifier is used to make predictions based solely on these
attribution maps. We compare Attri-Net to five post-hoc explanation techniques
and one inherently interpretable classifier on three chest X-ray datasets. We
find that Attri-Net produces high-quality multi-label explanations consistent
with clinical knowledge and has comparable classification performance to
state-of-the-art classification models.Comment: Accepted to MIDL 202
- …