299 research outputs found
Is the study of Machine Learning distracting for Neurosciences?
International audienc
Sharing Data and Image Processing Pipelines: The Information Analysis & Management initiative
International audienc
Machine Learning & Neurosciences
International audienc
Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach
In standard clinical within-subject analyses of event-related fMRI data, two
steps are usually performed separately: detection of brain activity and
estimation of the hemodynamic response. Because these two steps are inherently
linked, we adopt the so-called region-based Joint Detection-Estimation (JDE)
framework that addresses this joint issue using a multivariate inference for
detection and estimation. JDE is built by making use of a regional bilinear
generative model of the BOLD response and constraining the parameter estimation
by physiological priors using temporal and spatial information in a Markovian
modeling. In contrast to previous works that use Markov Chain Monte Carlo
(MCMC) techniques to approximate the resulting intractable posterior
distribution, we recast the JDE into a missing data framework and derive a
Variational Expectation-Maximization (VEM) algorithm for its inference. A
variational approximation is used to approximate the Markovian model in the
unsupervised spatially adaptive JDE inference, which allows fine automatic
tuning of spatial regularisation parameters. It follows a new algorithm that
exhibits interesting properties compared to the previously used MCMC-based
approach. Experiments on artificial and real data show that VEM-JDE is robust
to model mis-specification and provides computational gain while maintaining
good performance in terms of activation detection and hemodynamic shape
recovery
Multi-layer Aggregation as a key to feature-based OOD detection
Deep Learning models are easily disturbed by variations in the input images
that were not observed during the training stage, resulting in unpredictable
predictions. Detecting such Out-of-Distribution (OOD) images is particularly
crucial in the context of medical image analysis, where the range of possible
abnormalities is extremely wide. Recently, a new category of methods has
emerged, based on the analysis of the intermediate features of a trained model.
These methods can be divided into 2 groups: single-layer methods that consider
the feature map obtained at a fixed, carefully chosen layer, and multi-layer
methods that consider the ensemble of the feature maps generated by the model.
While promising, a proper comparison of these algorithms is still lacking. In
this work, we compared various feature-based OOD detection methods on a large
spectra of OOD (20 types), representing approximately 7800 3D MRIs. Our
experiments shed the light on two phenomenons. First, multi-layer methods
consistently outperform single-layer approaches, which tend to have
inconsistent behaviour depending on the type of anomaly. Second, the OOD
detection performance highly depends on the architecture of the underlying
neural network.Comment: Accepted for presentation at the Workshop on Uncertainty for Safe
Utilization of Machine Learning in Medical Imaging (UNSURE) at MICCAI 202
TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images
The volume of a brain lesion (e.g. infarct or tumor) is a powerful indicator
of patient prognosis and can be used to guide the therapeutic strategy.
Lesional volume estimation is usually performed by segmentation with deep
convolutional neural networks (CNN), currently the state-of-the-art approach.
However, to date, few work has been done to equip volume segmentation tools
with adequate quantitative predictive intervals, which can hinder their
usefulness and acceptation in clinical practice. In this work, we propose
TriadNet, a segmentation approach relying on a multi-head CNN architecture,
which provides both the lesion volumes and the associated predictive intervals
simultaneously, in less than a second. We demonstrate its superiority over
other solutions on BraTS 2021, a large-scale MRI glioblastoma image database.Comment: Accepted for presentation at the Workshop on Uncertainty for Safe
Utilization of Machine Learning in Medical Imaging (UNSURE) at MICCAI 202
Towards frugal unsupervised detection of subtle abnormalities in medical imaging
Anomaly detection in medical imaging is a challenging task in contexts where
abnormalities are not annotated. This problem can be addressed through
unsupervised anomaly detection (UAD) methods, which identify features that do
not match with a reference model of normal profiles. Artificial neural networks
have been extensively used for UAD but they do not generally achieve an optimal
trade-o between accuracy and computational demand. As an
alternative, we investigate mixtures of probability distributions whose
versatility has been widely recognized for a variety of data and tasks, while
not requiring excessive design eort or tuning. Their
expressivity makes them good candidates to account for complex multivariate
reference models. Their much smaller number of parameters makes them more
amenable to interpretation and e cient learning. However, standard estimation
procedures, such as the Expectation-Maximization algorithm, do not scale well
to large data volumes as they require high memory usage. To address this issue,
we propose to incrementally compute inferential quantities. This online
approach is illustrated on the challenging detection of subtle abnormalities in
MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The
identified structural abnormalities are consistent with the disease
progression, as accounted by the Hoehn and Yahr scale
Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package
For statistical analysis of functional magnetic resonance imaging (fMRI) data sets, we propose a data-driven approach based on independent component analysis (ICA) implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the computationally tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attractive exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package
Graph-based methods coupled with specific distributional distances for adversarial attack detection
Artificial neural networks are prone to being fooled by carefully perturbed
inputs which cause an egregious misclassification. These \textit{adversarial}
attacks have been the focus of extensive research. Likewise, there has been an
abundance of research in ways to detect and defend against them. We introduce a
novel approach of detection and interpretation of adversarial attacks from a
graph perspective. For an image, benign or adversarial, we study how a neural
network's architecture can induce an associated graph. We study this graph and
introduce specific measures used to predict and interpret adversarial attacks.
We show that graphs-based approaches help to investigate the inner workings of
adversarial attacks
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