35,500 research outputs found
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks
Meta-learning has recently been an emerging data-efficient learning technique
for various medical imaging operations and has helped advance contemporary deep
learning models. Furthermore, meta-learning enhances the knowledge
generalization of the imaging tasks by learning both shared and discriminative
weights for various configurations of imaging tasks. However, existing
meta-learning models attempt to learn a single set of weight initializations of
a neural network that might be restrictive for multimodal data. This work aims
to develop a multimodal meta-learning model for image reconstruction, which
augments meta-learning with evolutionary capabilities to encompass diverse
acquisition settings of multimodal data. Our proposed model called KM-MAML
(Kernel Modulation-based Multimodal Meta-Learning), has hypernetworks that
evolve to generate mode-specific weights. These weights provide the
mode-specific inductive bias for multiple modes by re-calibrating each kernel
of the base network for image reconstruction via a low-rank kernel modulation
operation. We incorporate gradient-based meta-learning (GBML) in the contextual
space to update the weights of the hypernetworks for different modes. The
hypernetworks and the reconstruction network in the GBML setting provide
discriminative mode-specific features and low-level image features,
respectively. Experiments on multi-contrast MRI reconstruction show that our
model, (i) exhibits superior reconstruction performance over joint training,
other meta-learning methods, and context-specific MRI reconstruction methods,
and (ii) better adaptation capabilities with improvement margins of 0.5 dB in
PSNR and 0.01 in SSIM. Besides, a representation analysis with U-Net shows that
kernel modulation infuses 80% of mode-specific representation changes in the
high-resolution layers. Our source code is available at
https://github.com/sriprabhar/KM-MAML/.Comment: Accepted for publication in Elsevier Applied Soft Computing Journal,
36 pages, 18 figure
Prediction of Thrombectomy Functional Outcomes using Multimodal Data
Recent randomised clinical trials have shown that patients with ischaemic
stroke {due to occlusion of a large intracranial blood vessel} benefit from
endovascular thrombectomy. However, predicting outcome of treatment in an
individual patient remains a challenge. We propose a novel deep learning
approach to directly exploit multimodal data (clinical metadata information,
imaging data, and imaging biomarkers extracted from images) to estimate the
success of endovascular treatment. We incorporate an attention mechanism in our
architecture to model global feature inter-dependencies, both channel-wise and
spatially. We perform comparative experiments using unimodal and multimodal
data, to predict functional outcome (modified Rankin Scale score, mRS) and
achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy
for individual mRS scores.Comment: Accepted at Medical Image Understanding and Analysis (MIUA) 202
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance
(MR) images for guiding targeted prostate biopsy has significantly improved the
biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image
registration. However, it is very challenging to obtain a robust automatic
MR-TRUS registration due to the large appearance difference between the two
imaging modalities. The work presented in this paper aims to tackle this
problem by addressing two challenges: (i) the definition of a suitable
similarity metric and (ii) the determination of a suitable optimization
strategy.
Methods: This work proposes the use of a deep convolutional neural network to
learn a similarity metric for MR-TRUS registration. We also use a composite
optimization strategy that explores the solution space in order to search for a
suitable initialization for the second-order optimization of the learned
metric. Further, a multi-pass approach is used in order to smooth the metric
for optimization.
Results: The learned similarity metric outperforms the classical mutual
information and also the state-of-the-art MIND feature based methods. The
results indicate that the overall registration framework has a large capture
range. The proposed deep similarity metric based approach obtained a mean TRE
of 3.86mm (with an initial TRE of 16mm) for this challenging problem.
Conclusion: A similarity metric that is learned using a deep neural network
can be used to assess the quality of any given image registration and can be
used in conjunction with the aforementioned optimization framework to perform
automatic registration that is robust to poor initialization.Comment: To appear on IJCAR
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