1,515 research outputs found

    Artificial Intelligence with Light Supervision: Application to Neuroimaging

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    Recent developments in artificial intelligence research have resulted in tremendous success in computer vision, natural language processing and medical imaging tasks, often reaching human or superhuman performance. In this thesis, I further developed artificial intelligence methods based on convolutional neural networks with a special focus on the automated analysis of brain magnetic resonance imaging scans (MRI). I showed that efficient artificial intelligence systems can be created using only minimal supervision, by reducing the quantity and quality of annotations used for training. I applied those methods to the automated assessment of the burden of enlarged perivascular spaces, brain structural changes that may be related to dementia, stroke, mult

    Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

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    Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in `Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out `lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future

    Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

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    With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho

    Automated medical diagnosis of alzheimer´s disease using an Efficient Net convolutional neural network

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    Producción CientíficaAlzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach—"fusion of end-to-end and transfer learning"—to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

    Deep Interpretability Methods for Neuroimaging

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    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

    Multi-view machine learning methods to uncover brain-behaviour associations

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    The heterogeneity of neurological and mental disorders has been a key confound in disease understanding and treatment outcome prediction, as the study of patient populations typically includes multiple subgroups that do not align with the diagnostic categories. The aim of this thesis is to investigate and extend classical multivariate methods, such as Canonical Correlation Analysis (CCA), and latent variable models, e.g., Group Factor Analysis (GFA), to uncover associations between brain and behaviour that may characterize patient populations and subgroups of patients. In the first contribution of this thesis, we applied CCA to investigate brain-behaviour associations in a sample of healthy and depressed adolescents and young adults. We found two positive-negative brain-behaviour modes of covariation, capturing externalisation/ internalisation symptoms and well-being/distress. In the second contribution of the thesis, I applied sparse CCA to the same dataset to present a regularised approach to investigate brain-behaviour associations in high dimensional datasets. Here, I compared two approaches to optimise the regularisation parameters of sparse CCA and showed that the choice of the optimisation strategy might have an impact on the results. In the third contribution, I extended the GFA model to mitigate some limitations of CCA, such as handling missing data. I applied the extended GFA model to investigate links between high dimensional brain imaging and non-imaging data from the Human Connectome Project, and predict non-imaging measures from brain functional connectivity. The results were consistent between complete and incomplete data, and replicated previously reported findings. In the final contribution of this thesis, I proposed two extensions of GFA to uncover brain behaviour associations that characterize subgroups of subjects in an unsupervised and supervised way, as well as explore within-group variability at the individual level. These extensions were demonstrated using a dataset of patients with genetic frontotemporal dementia. In summary, this thesis presents multi-view methods that can be used to deepen our understanding about the latent dimensions of disease in mental/neurological disorders and potentially enable patient stratification

    Is attention all you need in medical image analysis? A review

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    Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. The main disadvantage of typical CNN models is that they ignore global pixel relationships within images, which limits their generalisation ability to understand out-of-distribution data with different 'global' information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced a comprehensive analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated

    DEEP-AD: The deep learning model for diagnostic classification and prognostic prediction of alzheimer's disease

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    In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical judgment regarding the early detection of AD by using DL. To that aim, the system design research methodology is suggested in this dissertation for achieving three goals. The first goal is to investigate the DL models that have performed well at identifying patterns associated with AD, as well as the accuracy so far attained, limitations, and gaps. A systematic review of the literature (SLR) revealed a shortage of empirical studies on the early identification of AD through DL. In this regard, thirteen empirical studies were identified and examined. We concluded that three-dimensional (3D) DL models have been generated far less often and that their performance is also inadequate to qualify them for clinical trials. The second goal is to provide the neuroradiologist with the computer-interpretable information they need to analyze neuroimaging biomarkers. Given this context, the next step in this dissertation is to find the optimum DL model to analyze neuroimaging biomarkers. It has been achieved in two steps. In the first step, eight state-of-the-art DL models have been implemented by training from scratch using end-to-end learning (E2EL) for two binary classification tasks (AD vs. CN and AD vs. stable MCI) and compared by utilizing MRI scans from the publicly accessible datasets of neuroimaging biomarkers. Comparative analysis is carried out by utilizing efficiency-effects graphs, comprehensive indicators, and ranking mechanisms. For the training of the AD vs. sMCI task, the EfficientNet-B0 model gets the highest value for the comprehensive indicator and has the fewest parameters. DenseNet264 performed better than the others in terms of evaluation matrices, but since it has the most parameters, it costs more to train. For the AD vs. CN task by DenseNet264, we achieved 100% accuracy for training and 99.56% accuracy for testing. However, the classification accuracy was still only 82.5% for the AD vs. sMCI task. In the second step, fusion of transfer learning (TL) with E2EL is applied to train the EfficientNet-B0 for the AD vs. sMCI task, which achieved 95.29% accuracy for training and 93.10% accuracy for testing. Additionally, we have also implemented EfficientNet-B0 for the multiclass AD vs. CN vs. sMCI classification task with E2EL to be used in ensemble of models and achieved 85.66% training accuracy and 87.38% testing accuracy. To evaluate the model’s robustness, neuroradiologists must validate the implemented model. As a result, the third goal of this dissertation is to create a tool that neuroradiologists may use at their convenience. To achieve this objective, this dissertation proposes a web-based application (DEEP-AD) that has been created by making an ensemble of Efficient-Net B0 and DenseNet 264 (based on the contribution of goal 2). The accuracy of a DEEP-AD prototype has undergone repeated evaluation and improvement. First, we validated 41 subjects of Spanish MRI datasets (acquired from HT Medica, Madrid, Spain), achieving an accuracy of 82.90%, which was later verified by neuroradiologists. The results of these evaluation studies showed the accomplishment of such goals and relevant directions for future research in applied DL for the early detection of AD in clinical settings.En términos de contexto, el objetivo de esta tesis es ayudar a los neurorradiólogos en su juicio clínico sobre la detección precoz de la AD mediante el uso de DL. Para ello, en esta tesis se propone la metodología de investigación de diseño de sistemas para lograr tres objetivos. El segundo objetivo es proporcionar al neurorradiólogo la información interpretable por ordenador que necesita para analizar los biomarcadores de neuroimagen. Dado este contexto, el siguiente paso en esta tesis es encontrar el modelo DL óptimo para analizar biomarcadores de neuroimagen. Esto se ha logrado en dos pasos. En el primer paso, se han implementado ocho modelos DL de última generación mediante entrenamiento desde cero utilizando aprendizaje de extremo a extremo (E2EL) para dos tareas de clasificación binarias (AD vs. CN y AD vs. MCI estable) y se han comparado utilizando escaneos MRI de los conjuntos de datos de biomarcadores de neuroimagen de acceso público. El análisis comparativo se lleva a cabo utilizando gráficos de efecto-eficacia, indicadores exhaustivos y mecanismos de clasificación. Para el entrenamiento de la tarea AD vs. sMCI, el modelo EfficientNet-B0 obtiene el valor más alto para el indicador exhaustivo y tiene el menor número de parámetros. DenseNet264 obtuvo mejores resultados que los demás en términos de matrices de evaluación, pero al ser el que tiene más parámetros, su entrenamiento es más costoso. Para la tarea AD vs. CN de DenseNet264, conseguimos una accuracy del 100% en el entrenamiento y del 99,56% en las pruebas. Sin embargo, la accuracy de la clasificación fue sólo del 82,5% para la tarea AD vs. sMCI. En el segundo paso, se aplica la fusión del aprendizaje por transferencia (TL) con E2EL para entrenar la EfficientNet-B0 para la tarea AD vs. sMCI, que alcanzó una accuracy del 95,29% en el entrenamiento y del 93,10% en las pruebas. Además, también hemos implementado EfficientNet-B0 para la tarea de clasificación multiclase AD vs. CN vs. sMCI con E2EL para su uso en conjuntos de modelos y hemos obtenido una accuracy de entrenamiento del 85,66% y una precisión de prueba del 87,38%. Para evaluar la solidez del modelo, los neurorradiólogos deben validar el modelo implementado. Como resultado, el tercer objetivo de esta disertación es crear una herramienta que los neurorradiólogos puedan utilizar a su conveniencia. Para lograr este objetivo, esta disertación propone una aplicación basada en web (DEEP-AD) que ha sido creada haciendo un ensemble de Efficient-Net B0 y DenseNet 264 (basado en la contribución del objetivo 2). La accuracy del prototipo DEEP-AD ha sido sometida a repetidas evaluaciones y mejoras. En primer lugar, validamos 41 sujetos de conjuntos de datos de MRI españoles (adquiridos de HT Medica, Madrid, España), logrando una accuracy del 82,90%, que posteriormente fue verificada por neurorradiólogos. Los resultados de estos estudios de evaluación mostraron el cumplimiento de dichos objetivos y las direcciones relevantes para futuras investigaciones en DL, aplicada en la detección precoz de la AD en entornos clínicos.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione

    3D Deep Learning on Medical Images: A Review

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    The rapid advancements in machine learning, graphics processing technologies and availability of medical imaging data has led to a rapid increase in use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, give a brief mathematical description of 3D CNN and the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection, and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models, in general) and possible future trends in the field.Comment: 13 pages, 4 figures, 2 table
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