135 research outputs found
End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification
As shown in computer vision, the power of deep learning lies in automatically
learning relevant and powerful features for any perdition task, which is made
possible through end-to-end architectures. However, deep learning approaches
applied for classifying medical images do not adhere to this architecture as
they rely on several pre- and post-processing steps. This shortcoming can be
explained by the relatively small number of available labeled subjects, the
high dimensionality of neuroimaging data, and difficulties in interpreting the
results of deep learning methods. In this paper, we propose a simple 3D
Convolutional Neural Networks and exploit its model parameters to tailor the
end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our
model can diagnose AD with an accuracy of 94.1\% on the popular ADNI dataset
using only MRI data, which outperforms the previous state-of-the-art. Based on
the learned model, we identify the disease biomarkers, the results of which
were in accordance with the literature. We further transfer the learned model
to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which
yield better results compared to other methods
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models
Segmentation is a fundamental task for extracting semantically meaningful
regions from an image. The goal of segmentation algorithms is to accurately
assign object labels to each image location. However, image-noise, shortcomings
of algorithms, and image ambiguities cause uncertainty in label assignment.
Estimating the uncertainty in label assignment is important in multiple
application domains, such as segmenting tumors from medical images for
radiation treatment planning. One way to estimate these uncertainties is
through the computation of posteriors of Bayesian models, which is
computationally prohibitive for many practical applications. On the other hand,
most computationally efficient methods fail to estimate label uncertainty. We
therefore propose in this paper the Active Mean Fields (AMF) approach, a
technique based on Bayesian modeling that uses a mean-field approximation to
efficiently compute a segmentation and its corresponding uncertainty. Based on
a variational formulation, the resulting convex model combines any
label-likelihood measure with a prior on the length of the segmentation
boundary. A specific implementation of that model is the Chan-Vese segmentation
model (CV), in which the binary segmentation task is defined by a Gaussian
likelihood and a prior regularizing the length of the segmentation boundary.
Furthermore, the Euler-Lagrange equations derived from the AMF model are
equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image
denoising. Solutions to the AMF model can thus be implemented by directly
utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We
qualitatively assess the approach on synthetic data as well as on real natural
and medical images. For a quantitative evaluation, we apply our approach to the
icgbench dataset
Generating Realistic 3D Brain MRIs Using a Conditional Diffusion Probabilistic Model
Training deep learning models on brain MRI is often plagued by small sample
size, which can lead to biased training or overfitting. One potential solution
is to synthetically generate realistic MRIs via generative models such as
Generative Adversarial Network (GAN). However, existing GANs for synthesizing
realistic brain MRIs largely rely on image-to-image conditioned transformations
requiring extensive, well-curated pairs of MRI samples for training. On the
other hand, unconditioned GAN models (i.e., those generating MRI from random
noise) are unstable during training and tend to produce blurred images during
inference. Here, we propose an efficient strategy that generates high fidelity
3D brain MRI via Diffusion Probabilistic Model (DPM). To this end, we train a
conditional DPM with attention to generate an MRI sub-volume (a set of slices
at arbitrary locations) conditioned on another subset of slices from the same
MRI. By computing attention weights from slice indices and using a mask to
encode the target and conditional slices, the model is able to learn the
long-range dependency across distant slices with limited computational
resources. After training, the model can progressively synthesize a new 3D
brain MRI by generating the first subset of slices from random noise and
conditionally generating subsequent slices. Based on 1262 t1-weighted MRIs from
three neuroimaging studies, our experiments demonstrate that the proposed
method can generate high quality 3D MRIs that share the same distribution as
real MRIs and are more realistic than the ones produced by GAN-based models
An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment
One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive
loss of postural reflexes, which eventually leads to gait difficulties and
balance problems. Identifying disruptions in brain function associated with
gait impairment could be crucial in better understanding PD motor progression,
thus advancing the development of more effective and personalized therapeutics.
In this work, we present an explainable, geometric, weighted-graph attention
neural network (xGW-GAT) to identify functional networks predictive of the
progression of gait difficulties in individuals with PD. xGW-GAT predicts the
multi-class gait impairment on the MDS Unified PD Rating Scale (MDS-UPDRS). Our
computational- and data-efficient model represents functional connectomes as
symmetric positive definite (SPD) matrices on a Riemannian manifold to
explicitly encode pairwise interactions of entire connectomes, based on which
we learn an attention mask yielding individual- and group-level explainability.
Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals
with PD, xGW-GAT identifies functional connectivity patterns associated with
gait impairment in PD and offers interpretable explanations of functional
subnetworks associated with motor impairment. Our model successfully
outperforms several existing methods while simultaneously revealing
clinically-relevant connectivity patterns. The source code is available at
https://github.com/favour-nerrise/xGW-GAT .Comment: Accepted by the 26th International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI 2023). MICCAI
Student-Author Registration (STAR) Award. 11 pages, 2 figures, 1 table,
appendix. Source Code: https://github.com/favour-nerrise/xGW-GA
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