26 research outputs found
Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review
Autism spectrum disorder (ASD) is a brain condition characterized by diverse
signs and symptoms that appear in early childhood. ASD is also associated with
communication deficits and repetitive behavior in affected individuals. Various
ASD detection methods have been developed, including neuroimaging modalities
and psychological tests. Among these methods, magnetic resonance imaging (MRI)
imaging modalities are of paramount importance to physicians. Clinicians rely
on MRI modalities to diagnose ASD accurately. The MRI modalities are
non-invasive methods that include functional (fMRI) and structural (sMRI)
neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI
for specialists is often laborious and time-consuming; therefore, several
computer-aided design systems (CADS) based on artificial intelligence (AI) have
been developed to assist the specialist physicians. Conventional machine
learning (ML) and deep learning (DL) are the most popular schemes of AI used
for diagnosing ASD. This study aims to review the automated detection of ASD
using AI. We review several CADS that have been developed using ML techniques
for the automated diagnosis of ASD using MRI modalities. There has been very
limited work on the use of DL techniques to develop automated diagnostic models
for ASD. A summary of the studies developed using DL is provided in the
appendix. Then, the challenges encountered during the automated diagnosis of
ASD using MRI and AI techniques are described in detail. Additionally, a
graphical comparison of studies using ML and DL to diagnose ASD automatically
is discussed. We conclude by suggesting future approaches to detecting ASDs
using AI techniques and MRI neuroimaging
Deep Interpretability Methods for Neuroimaging
Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Nevertheless, the difficulty of reliable training on high-dimensional but small-sample datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this dissertation, we address these challenges by proposing a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. The developed model is pre-trainable and alleviates the need to collect an enormous amount of neuroimaging samples to achieve optimal training.
We also provide a quantitative validation module, Retain and Retrain (RAR), that can objectively verify the higher predictability of the dynamics learned by the model. Results successfully demonstrate that the proposed framework enables learning the fMRI dynamics directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction. We also comprehensively reviewed deep interpretability literature in the neuroimaging domain. Our analysis reveals the ongoing trend of interpretability practices in neuroimaging studies and identifies the gaps that should be addressed for effective human-machine collaboration in this domain.
This dissertation also proposed a post hoc interpretability method, Geometrically Guided Integrated Gradients (GGIG), that leverages geometric properties of the functional space as learned by a deep learning model. With extensive experiments and quantitative validation on MNIST and ImageNet datasets, we demonstrate that GGIG outperforms integrated gradients (IG), which is considered to be a popular interpretability method in the literature. As GGIG is able to identify the contours of the discriminative regions in the input space, GGIG may be useful in various medical imaging tasks where fine-grained localization as an explanation is beneficial
Decoding Task-Based fMRI Data Using Graph Neural Networks, Considering Individual Differences
Functional magnetic resonance imaging (fMRI) is a non-invasive technology that provides high spatial resolution in determining the human brain\u27s responses and measures regional brain activity through metabolic changes in blood oxygen consumption associated with neural activity. Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific task performance. Over the past several years, a variety of computational methods have been proposed to decode task fMRI data that can identify brain regions associated with different task stimulations. Despite the advances made by these methods, several limitations exist due to graph representations and graph embeddings transferred from task fMRI signals. In the present study, we proposed an end-to-end graph convolutional network by combining the convolutional neural network with graph representation, with three convolutional layers to classify task fMRI data from the Human Connectome Project (302 participants, 22–35 years of age). One goal of this dissertation was to improve classification performance. We applied four of the most widely used node embedding algorithms—NetMF, RandNE, Node2Vec, and Walklets—to automatically extract the structural properties of the nodes in the brain functional graph, then evaluated the performance of the classification model. The empirical results indicated that the proposed GCN framework accurately identified the brain\u27s state in task fMRI data and achieved comparable macro F1 scores of 0.978 and 0.976 with the NetMF and RandNE embedding methods, respectively. Another goal of the dissertation was to assess the effects of individual differences (i.e., gender and fluid intelligence) on classification performance. We tested the proposed GCN framework on sub-datasets divided according to gender and fluid intelligence. Experimental results indicated significant differences in the classification predictions of gender, but not high/low fluid intelligence fMRI data. Our experiments yielded promising results and demonstrated the superior ability of our GCN in modeling task fMRI data
Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9
International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications
Multi-Kernel Capsule Network for Schizophrenia Identification
Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multi-kernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match with partition sizes of brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of widely-used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multi-kernel capsule structure with consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification
Dealing with heterogeneity in the prediction of clinical diagnosis
Le diagnostic assisté par ordinateur est un domaine de recherche en émergence et se situe
à l’intersection de l’imagerie médicale et de l’apprentissage machine. Les données médi-
cales sont de nature très hétérogène et nécessitent une attention particulière lorsque l’on
veut entraîner des modèles de prédiction. Dans cette thèse, j’ai exploré deux sources
d’hétérogénéité, soit l’agrégation multisites et l’hétérogénéité des étiquettes cliniques
dans le contexte de l’imagerie par résonance magnétique (IRM) pour le diagnostic de la
maladie d’Alzheimer (MA). La première partie de ce travail consiste en une introduction
générale sur la MA, l’IRM et les défis de l’apprentissage machine en imagerie médicale.
Dans la deuxième partie de ce travail, je présente les trois articles composant la thèse.
Enfin, la troisième partie porte sur une discussion des contributions et perspectives fu-
tures de ce travail de recherche. Le premier article de cette thèse montre que l’agrégation
des données sur plusieurs sites d’acquisition entraîne une certaine perte, comparative-
ment à l’analyse sur un seul site, qui tend à diminuer plus la taille de l’échantillon aug-
mente. Le deuxième article de cette thèse examine la généralisabilité des modèles de
prédiction à l’aide de divers schémas de validation croisée. Les résultats montrent que
la formation et les essais sur le même ensemble de sites surestiment la précision du
modèle, comparativement aux essais sur des nouveaux sites. J’ai également montré que
l’entraînement sur un grand nombre de sites améliore la précision sur des nouveaux sites.
Le troisième et dernier article porte sur l’hétérogénéité des étiquettes cliniques et pro-
pose un nouveau cadre dans lequel il est possible d’identifier un sous-groupe d’individus
qui partagent une signature homogène hautement prédictive de la démence liée à la MA.
Cette signature se retrouve également chez les patients présentant des symptômes mod-
érés. Les résultats montrent que 90% des sujets portant la signature ont progressé vers
la démence en trois ans. Les travaux de cette thèse apportent ainsi de nouvelles con-
tributions à la manière dont nous approchons l’hétérogénéité en diagnostic médical et
proposent des pistes de solution pour tirer profit de cette hétérogénéité.Computer assisted diagnosis has emerged as a popular area of research at the intersection
of medical imaging and machine learning. Medical data are very heterogeneous in nature
and therefore require careful attention when one wants to train prediction models. In
this thesis, I explored two sources of heterogeneity, multisite aggregation and clinical
label heterogeneity, in an application of magnetic resonance imaging to the diagnosis
of Alzheimer’s disease. In the process, I learned about the feasibility of multisite data
aggregation and how to leverage that heterogeneity in order to improve generalizability
of prediction models. Part one of the document is a general context introduction to
Alzheimer’s disease, magnetic resonance imaging, and machine learning challenges in
medical imaging. In part two, I present my research through three articles (two published
and one in preparation). Finally, part three provides a discussion of my contributions
and hints to possible future developments. The first article shows that data aggregation
across multiple acquisition sites incurs some loss, compared to single site analysis, that
tends to diminish as the sample size increase. These results were obtained through semisynthetic
Monte-Carlo simulations based on real data. The second article investigates the
generalizability of prediction models with various cross-validation schemes. I showed
that training and testing on the same batch of sites over-estimates the accuracy of the
model, compared to testing on unseen sites. However, I also showed that training on a
large number of sites improves the accuracy on unseen sites. The third article, on clinical
label heterogeneity, proposes a new framework where we can identify a subgroup of
individuals that share a homogeneous signature highly predictive of AD dementia. That
signature could also be found in patients with mild symptoms, 90% of whom progressed
to dementia within three years. The thesis thus makes new contributions to dealing
with heterogeneity in medical diagnostic applications and proposes ways to leverage
that heterogeneity to our benefit