218 research outputs found
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
The performance of multi-task learning in Convolutional Neural Networks
(CNNs) hinges on the design of feature sharing between tasks within the
architecture. The number of possible sharing patterns are combinatorial in the
depth of the network and the number of tasks, and thus hand-crafting an
architecture, purely based on the human intuitions of task relationships can be
time-consuming and suboptimal. In this paper, we present a probabilistic
approach to learning task-specific and shared representations in CNNs for
multi-task learning. Specifically, we propose "stochastic filter groups''
(SFG), a mechanism to assign convolution kernels in each layer to "specialist''
or "generalist'' groups, which are specific to or shared across different
tasks, respectively. The SFG modules determine the connectivity between layers
and the structures of task-specific and shared representations in the network.
We employ variational inference to learn the posterior distribution over the
possible grouping of kernels and network parameters. Experiments demonstrate
that the proposed method generalises across multiple tasks and shows improved
performance over baseline methods.Comment: Accepted for oral presentation at ICCV 201
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Multi-task neural network architectures provide a mechanism that jointly
integrates information from distinct sources. It is ideal in the context of
MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT)
scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic
multi-task network that estimates: 1) intrinsic uncertainty through a
heteroscedastic noise model for spatially-adaptive task loss weighting and 2)
parameter uncertainty through approximate Bayesian inference. This allows
sampling of multiple segmentations and synCTs that share their network
representation. We test our model on prostate cancer scans and show that it
produces more accurate and consistent synCTs with a better estimation in the
variance of the errors, state of the art results in OAR segmentation and a
methodology for quality assurance in radiotherapy treatment planning.Comment: Early-accept at MICCAI 2018, 8 pages, 4 figure
Denoising diffusion models for out-of-distribution detection
Out-of-distribution detection is crucial to the safe deployment of machine
learning systems. Currently, unsupervised out-of-distribution detection is
dominated by generative-based approaches that make use of estimates of the
likelihood or other measurements from a generative model. Reconstruction-based
methods offer an alternative approach, in which a measure of reconstruction
error is used to determine if a sample is out-of-distribution. However,
reconstruction-based approaches are less favoured, as they require careful
tuning of the model's information bottleneck - such as the size of the latent
dimension - to produce good results. In this work, we exploit the view of
denoising diffusion probabilistic models (DDPM) as denoising autoencoders where
the bottleneck is controlled externally, by means of the amount of noise
applied. We propose to use DDPMs to reconstruct an input that has been noised
to a range of noise levels, and use the resulting multi-dimensional
reconstruction error to classify out-of-distribution inputs. We validate our
approach both on standard computer-vision datasets and on higher dimension
medical datasets. Our approach outperforms not only reconstruction-based
methods, but also state-of-the-art generative-based approaches. Code is
available at https://github.com/marksgraham/ddpm-ood
Hierarchical brain parcellation with uncertainty
Many atlases used for brain parcellation are hierarchically organised,
progressively dividing the brain into smaller sub-regions. However,
state-of-the-art parcellation methods tend to ignore this structure and treat
labels as if they are `flat'. We introduce a hierarchically-aware brain
parcellation method that works by predicting the decisions at each branch in
the label tree. We further show how this method can be used to model
uncertainty separately for every branch in this label tree. Our method exceeds
the performance of flat uncertainty methods, whilst also providing decomposed
uncertainty estimates that enable us to obtain self-consistent parcellations
and uncertainty maps at any level of the label hierarchy. We demonstrate a
simple way these decision-specific uncertainty maps may be used to provided
uncertainty-thresholded tissue maps at any level of the label tree.Comment: To be published in the MICCAI 2020 workshop: Uncertainty for Safe
Utilization of Machine Learning in Medical Imagin
PET Reconstruction With an Anatomical MRI Prior Using Parallel Level Sets.
The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) offers unique possibilities. In this paper we aim to exploit the high spatial resolution of MRI to enhance the reconstruction of simultaneously acquired PET data. We propose a new prior to incorporate structural side information into a maximum a posteriori reconstruction. The new prior combines the strengths of previously proposed priors for the same problem: it is very efficient in guiding the reconstruction at edges available from the side information and it reduces locally to edge-preserving total variation in the degenerate case when no structural information is available. In addition, this prior is segmentation-free, convex and no a priori assumptions are made on the correlation of edge directions of the PET and MRI images. We present results for a simulated brain phantom and for real data acquired by the Siemens Biograph mMR for a hardware phantom and a clinical scan. The results from simulations show that the new prior has a better trade-off between enhancing common anatomical boundaries and preserving unique features than several other priors. Moreover, it has a better mean absolute bias-to-mean standard deviation trade-off and yields reconstructions with superior relative l2-error and structural similarity index. These findings are underpinned by the real data results from a hardware phantom and a clinical patient confirming that the new prior is capable of promoting well-defined anatomical boundaries.This research was funded by the EPSRC (EP/K005278/1) and EP/H046410/1 and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. M.J.E was supported by an IMPACT studentship funded jointly by Siemens and the UCL Faculty of Engineering Sciences. K.T. and D.A. are partially supported by the EPSRC grant EP/M022587/1.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TMI.2016.254960
Presymptomatic cortical thinning in familial Alzheimer disease: A longitudinal MRI study.
OBJECTIVE: To identify a cortical signature pattern of cortical thinning in familial Alzheimer disease (FAD) and assess its utility in detecting and tracking presymptomatic neurodegeneration. METHODS: We recruited 43 FAD mutation carriers-36 PSEN1, 7 APP (20 symptomatic, 23 presymptomatic)-and 42 healthy controls to a longitudinal clinical and MRI study. T1-weighted MRI scans were acquired at baseline in all participants; 55 individuals (33 mutation carriers; 22 controls) had multiple (mean 2.9) follow-up scans approximately annually. Cortical thickness was measured using FreeSurfer. A cortical thinning signature was identified from symptomatic FAD participants. We then examined cortical thickness changes in this signature region in presymptomatic carriers and assessed associations with cognitive performance. RESULTS: The cortical signature included 6 regions: entorhinal cortex, inferior parietal cortex, precuneus, superior parietal cortex, superior frontal cortex, and supramarginal gyrus. There were significant differences in mean cortical signature thickness between mutation carriers and controls 3 years before predicted symptom onset. The earliest significant difference in a single region, detectable 4 years preonset, was in the precuneus. Rate of change in cortical thickness became significantly different in the cortical signature at 5 years before predicted onset, and in the precuneus at 8 years preonset. Baseline mean signature thickness predicted rate of subsequent thinning and correlated with presymptomatic cognitive change. CONCLUSIONS: The FAD cortical signature appears to be similar to that described for sporadic AD. All component regions showed significant presymptomatic thinning. A composite signature may provide more robust results than a single region and have utility as an outcome measure in presymptomatic trials
An optimized framework for quantitative magnetization transfer imaging of the cervical spinal cord in vivo
Purpose
To develop a framework to fully characterize quantitative magnetization transfer indices in the human cervical cord in vivo within a clinically feasible time.
Methods
A dedicated spinal cord imaging protocol for quantitative magnetization transfer was developed using a reduced field-of-view approach with echo planar imaging (EPI) readout. Sequence parameters were optimized based in the Cramer-Rao-lower bound. Quantitative model parameters (i.e., bound pool fraction, free and bound pool transverse relaxation times [ math formula, math formula], and forward exchange rate [kFB]) were estimated implementing a numerical model capable of dealing with the novelties of the sequence adopted. The framework was tested on five healthy subjects.
Results
Cramer-Rao-lower bound minimization produces optimal sampling schemes without requiring the establishment of a steady-state MT effect. The proposed framework allows quantitative voxel-wise estimation of model parameters at the resolution typically used for spinal cord imaging (i.e. 0.75 × 0.75 × 5 mm3), with a protocol duration of ∼35 min. Quantitative magnetization transfer parametric maps agree with literature values. Whole-cord mean values are: bound pool fraction = 0.11(±0.01), math formula = 46.5(±1.6) ms, math formula = 11.0(±0.2) µs, and kFB = 1.95(±0.06) Hz. Protocol optimization has a beneficial effect on reproducibility, especially for math formula and kFB.
Conclusion
The framework developed enables robust characterization of spinal cord microstructure in vivo using qMT. Magn Reson Med, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
Human anatomy, morphology, and associated diseases can be studied using
medical imaging data. However, access to medical imaging data is restricted by
governance and privacy concerns, data ownership, and the cost of acquisition,
thus limiting our ability to understand the human body. A possible solution to
this issue is the creation of a model able to learn and then generate synthetic
images of the human body conditioned on specific characteristics of relevance
(e.g., age, sex, and disease status). Deep generative models, in the form of
neural networks, have been recently used to create synthetic 2D images of
natural scenes. Still, the ability to produce high-resolution 3D volumetric
imaging data with correct anatomical morphology has been hampered by data
scarcity and algorithmic and computational limitations. This work proposes a
generative model that can be scaled to produce anatomically correct,
high-resolution, and realistic images of the human brain, with the necessary
quality to allow further downstream analyses. The ability to generate a
potentially unlimited amount of data not only enables large-scale studies of
human anatomy and pathology without jeopardizing patient privacy, but also
significantly advances research in the field of anomaly detection, modality
synthesis, learning under limited data, and fair and ethical AI. Code and
trained models are available at: https://github.com/AmigoLab/SynthAnatomy.Comment: 13 pages, 3 figures, 2 tables, accepted at SASHIMI MICCAI 202
Transformer-based out-of-distribution detection for clinically safe segmentation
In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular approach to safe processing is to train networks that can provide a measure of their uncertainty, but these tend to fail for inputs that are far outside the training data distribution. Recently, generative modelling approaches have been proposed as an alternative; these can quantify the likelihood of a data sample explicitly, filtering out any out-of-distribution (OOD) samples before further processing is performed. In this work, we focus on image segmentation and evaluate several approaches to network uncertainty in the far-OOD and near-OOD cases for the task of segmenting haemorrhages in head CTs. We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD. We propose performing full 3D OOD detection using a VQ-GAN to provide a compressed latent representation of the image and a transformer to estimate the data likelihood. Our approach successfully identifies images in both the far- and near-OOD cases. We find a strong relationship between image likelihood and the quality of a model’s segmentation, making this approach viable for filtering images unsuitable for segmentation. To our knowledge, this is the first time transformers have been applied to perform OOD detection on 3D image data.</p
- …