98 research outputs found
Computer-Aided Assessment of Tuberculosis with Radiological Imaging: From rule-based methods to Deep Learning
Mención Internacional en el tÃtulo de doctorTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb.)
that produces pulmonary damage due to its airborne nature. This fact facilitates the disease
fast-spreading, which, according to the World Health Organization (WHO), in 2021 caused
1.2 million deaths and 9.9 million new cases.
Traditionally, TB has been considered a binary disease (latent/active) due to the limited
specificity of the traditional diagnostic tests. Such a simple model causes difficulties in the
longitudinal assessment of pulmonary affectation needed for the development of novel drugs
and to control the spread of the disease.
Fortunately, X-Ray Computed Tomography (CT) images enable capturing specific manifestations
of TB that are undetectable using regular diagnostic tests, which suffer from
limited specificity. In conventional workflows, expert radiologists inspect the CT images.
However, this procedure is unfeasible to process the thousands of volume images belonging
to the different TB animal models and humans required for a suitable (pre-)clinical trial.
To achieve suitable results, automatization of different image analysis processes is a
must to quantify TB. It is also advisable to measure the uncertainty associated with this
process and model causal relationships between the specific mechanisms that characterize
each animal model and its level of damage. Thus, in this thesis, we introduce a set of novel
methods based on the state of the art Artificial Intelligence (AI) and Computer Vision (CV).
Initially, we present an algorithm to assess Pathological Lung Segmentation (PLS) employing
an unsupervised rule-based model which was traditionally considered a needed
step before biomarker extraction. This procedure allows robust segmentation in a Mtb. infection
model (Dice Similarity Coefficient, DSC, 94%±4%, Hausdorff Distance, HD,
8.64mm±7.36mm) of damaged lungs with lesions attached to the parenchyma and affected
by respiratory movement artefacts.
Next, a Gaussian Mixture Model ruled by an Expectation-Maximization (EM) algorithm
is employed to automatically quantify the burden of Mtb.using biomarkers extracted from the
segmented CT images. This approach achieves a strong correlation (R2 ≈ 0.8) between our
automatic method and manual extraction. Consequently, Chapter 3 introduces a model to automate the identification of TB lesions
and the characterization of disease progression. To this aim, the method employs the
Statistical Region Merging algorithm to detect lesions subsequently characterized by texture
features that feed a Random Forest (RF) estimator. The proposed procedure enables a
selection of a simple but powerful model able to classify abnormal tissue.
The latest works base their methodology on Deep Learning (DL). Chapter 4 extends
the classification of TB lesions. Namely, we introduce a computational model to infer
TB manifestations present in each lung lobe of CT scans by employing the associated
radiologist reports as ground truth. We do so instead of using the classical manually delimited
segmentation masks. The model adjusts the three-dimensional architecture, V-Net, to a multitask
classification context in which loss function is weighted by homoscedastic uncertainty.
Besides, the method employs Self-Normalizing Neural Networks (SNNs) for regularization.
Our results are promising with a Root Mean Square Error of 1.14 in the number of nodules
and F1-scores above 0.85 for the most prevalent TB lesions (i.e., conglomerations, cavitations,
consolidations, trees in bud) when considering the whole lung.
In Chapter 5, we present a DL model capable of extracting disentangled information from
images of different animal models, as well as information of the mechanisms that generate
the CT volumes. The method provides the segmentation mask of axial slices from three
animal models of different species employing a single trained architecture. It also infers the
level of TB damage and generates counterfactual images. So, with this methodology, we
offer an alternative to promote generalization and explainable AI models.
To sum up, the thesis presents a collection of valuable tools to automate the quantification
of pathological lungs and moreover extend the methodology to provide more explainable
results which are vital for drug development purposes. Chapter 6 elaborates on these
conclusions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidenta: MarÃa Jesús Ledesma Carbayo.- Secretario: David Expósito Singh.- Vocal: Clarisa Sánchez Gutiérre
Object Detection in medical imaging
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsArtificial Intelligence, assisted by deep learning, has emerged in various fields of our society. These systems allow the automation and the improvement of several tasks, even surpassing, in some cases, human capability. Object detection methods are used nowadays in several areas, including medical imaging analysis. However, these methods are susceptible to errors, and there is a lack of a universally accepted method that can be applied across all types of applications with the needed precision in the medical field. Additionally, the application of object detectors in medical imaging analysis has yet to be thoroughly analyzed to achieve a richer understanding of the state of the art.
To tackle these shortcomings, we present three studies with distinct goals. First, a quantitative and qualitative analysis of academic research was conducted to gather a perception of which object detectors are employed, the modality of medical imaging used, and the particular body parts under investigation. Secondly, we propose an optimized version of a widely used algorithm to overcome limitations commonly addressed in medical imaging by fine-tuning several hyperparameters. Thirdly, we develop a novel stacking approach to augment the precision of detections on medical imaging analysis.
The findings show that despite the late arrival of object detection in medical imaging analysis, the number of publications has increased in recent years, demonstrating the significant potential for growth. Additionally, we establish that it is possible to address some constraints on the data through an exhaustive optimization of the algorithm. Finally, our last study highlights that there is still room for improvement in these advanced techniques, using, as an example, stacking approaches.
The contributions of this dissertation are several, as it puts forward a deeper overview of the state-of-the-art applications of object detection algorithms in the medical field and presents strategies for addressing typical constraints in this area.A Inteligência Artificial, auxiliada pelo deep learning, tem emergido em diversas áreas da nossa sociedade. Estes sistemas permitem a automatização e a melhoria de diversas tarefas, superando mesmo, em alguns casos, a capacidade humana. Os métodos de detecção de objetos são utilizados atualmente em diversas áreas, inclusive na análise de imagens médicas. No entanto, esses métodos são suscetÃveis a erros e falta um método universalmente aceite que possa ser aplicado em todos os tipos de aplicações com a precisão necessária na área médica. Além disso, a aplicação de detectores de objetos na análise de imagens médicas ainda precisa ser analisada minuciosamente para alcançar uma compreensão mais rica do estado da arte.
Para enfrentar essas limitações, apresentamos três estudos com objetivos distintos. Inicialmente, uma análise quantitativa e qualitativa da pesquisa acadêmica foi realizada para obter uma percepção de quais detectores de objetos são empregues, a modalidade de imagem médica usada e as partes especÃficas do corpo sob investigação. Num segundo estudo, propomos uma versão otimizada de um algoritmo amplamente utilizado para superar limitações comumente abordadas em imagens médicas por meio do ajuste fino de vários hiperparâmetros. Em terceiro lugar, desenvolvemos uma nova abordagem de stacking para aumentar a precisão das detecções na análise de imagens médicas.
Os resultados demostram que, apesar da chegada tardia da detecção de objetos na análise de imagens médicas, o número de publicações aumentou nos últimos anos, evidenciando o significativo potencial de crescimento. Adicionalmente, estabelecemos que é possÃvel resolver algumas restrições nos dados por meio de uma otimização exaustiva do algoritmo. Finalmente, o nosso último estudo destaca que ainda há espaço para melhorias nessas técnicas avançadas, usando, como exemplo, abordagens de stacking.
As contribuições desta dissertação são várias, apresentando uma visão geral em maior detalhe das aplicações de ponta dos algoritmos de detecção de objetos na área médica e apresenta estratégias para lidar com restrições tÃpicas nesta área
Deep Learning in Medical Image Analysis
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
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
Deep Learning Models For Biomedical Data Analysis
The field of biomedical data analysis is a vibrant area of research dedicated to extracting valuable insights from a wide range of biomedical data sources, including biomedical images and genomics data. The emergence of deep learning, an artificial intelligence approach, presents significant prospects for enhancing biomedical data analysis and knowledge discovery. This dissertation focused on exploring innovative deep-learning methods for biomedical image processing and gene data analysis.
During the COVID-19 pandemic, biomedical imaging data, including CT scans and chest x-rays, played a pivotal role in identifying COVID-19 cases by categorizing patient chest x-ray outcomes as COVID-19-positive or negative. While supervised deep learning methods have effectively recognized COVID-19 patterns in chest x-ray datasets, the availability of annotated training data remains limited. To address this challenge, the thesis introduced a semi-supervised deep learning model named ssResNet, built upon the Residual Neural Network (ResNet) architecture. The model combines supervised and unsupervised paths, incorporating a weighted supervised loss function to manage data imbalance. The strategies to diminish prediction uncertainty in deep learning models for critical applications like medical image processing is explore. It achieves this through an ensemble deep learning model, integrating bagging deep learning and model calibration techniques. This ensemble model not only boosts biomedical image segmentation accuracy but also reduces prediction uncertainty, as validated on a comprehensive chest x-ray image segmentation dataset.
Furthermore, the thesis introduced an ensemble model integrating Proformer and ensemble learning methodologies. This model constructs multiple independent Proformers for predicting gene expression, their predictions are combined through weighted averaging to generate final predictions. Experimental outcomes underscore the efficacy of this ensemble model in enhancing prediction performance across various metrics.
In conclusion, this dissertation advances biomedical data analysis by harnessing the potential of deep learning techniques. It devises innovative approaches for processing biomedical images and gene data. By leveraging deep learning\u27s capabilities, this work paves the way for further progress in biomedical data analytics and its applications within clinical contexts.
Index Terms- biomedical data analysis, COVID-19, deep learning, ensemble learning, gene data analytics, medical image segmentation, prediction uncertainty, Proformer, Residual Neural Network (ResNet), semi-supervised learning
Self-supervised learning methods and applications in medical imaging analysis: A survey
The scarcity of high-quality annotated medical imaging datasets is a major
problem that collides with machine learning applications in the field of
medical imaging analysis and impedes its advancement. Self-supervised learning
is a recent training paradigm that enables learning robust representations
without the need for human annotation which can be considered an effective
solution for the scarcity of annotated medical data. This article reviews the
state-of-the-art research directions in self-supervised learning approaches for
image data with a concentration on their applications in the field of medical
imaging analysis. The article covers a set of the most recent self-supervised
learning methods from the computer vision field as they are applicable to the
medical imaging analysis and categorize them as predictive, generative, and
contrastive approaches. Moreover, the article covers 40 of the most recent
research papers in the field of self-supervised learning in medical imaging
analysis aiming at shedding the light on the recent innovation in the field.
Finally, the article concludes with possible future research directions in the
field
The Prominence of Artificial Intelligence in COVID-19
In December 2019, a novel virus called COVID-19 had caused an enormous number
of causalities to date. The battle with the novel Coronavirus is baffling and
horrifying after the Spanish Flu 2019. While the front-line doctors and medical
researchers have made significant progress in controlling the spread of the
highly contiguous virus, technology has also proved its significance in the
battle. Moreover, Artificial Intelligence has been adopted in many medical
applications to diagnose many diseases, even baffling experienced doctors.
Therefore, this survey paper explores the methodologies proposed that can aid
doctors and researchers in early and inexpensive methods of diagnosis of the
disease. Most developing countries have difficulties carrying out tests using
the conventional manner, but a significant way can be adopted with Machine and
Deep Learning. On the other hand, the access to different types of medical
images has motivated the researchers. As a result, a mammoth number of
techniques are proposed. This paper first details the background knowledge of
the conventional methods in the Artificial Intelligence domain. Following that,
we gather the commonly used datasets and their use cases to date. In addition,
we also show the percentage of researchers adopting Machine Learning over Deep
Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the
research challenges, we elaborate on the problems faced in COVID-19 research,
and we address the issues with our understanding to build a bright and healthy
environment.Comment: 63 pages, 3 tables, 17 figure
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