11 research outputs found

    Deep Learning in Medical Image Analysis

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    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

    Advanced Representation Learning for Dense Prediction Tasks in Medical Image Analysis

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    Machine learning is a rapidly growing field of artificial intelligence that allows computers to learn and make predictions using human labels. However, traditional machine learning methods have many drawbacks, such as being time-consuming, inefficient, task-specific biased, and requiring a large amount of domain knowledge. A subfield of machine learning, representation learning, focuses on learning meaningful and useful features or representations from input data. It aims to automatically learn relevant features from raw data, saving time, increasing efficiency and generalization, and reducing reliance on expert knowledge. Recently, deep learning has further accelerated the development of representation learning. It leverages deep architectures to extract complex and abstract representations, resulting in significant outperformance in many areas. In the field of computer vision, deep learning has made remarkable progress, particularly in high-level and real-world computer vision tasks. Since deep learning methods do not require handcrafted features and have the ability to understand complex visual information, they facilitate researchers to design automated systems that make accurate diagnoses and interpretations, especially in the field of medical image analysis. Deep learning has achieved state-of-the-art performance in many medical image analysis tasks, such as medical image regression/classification, generation and segmentation tasks. Compared to regression/classification tasks, medical image generation and segmentation tasks are more complex dense prediction tasks that understand semantic representations and generate pixel-level predictions. This thesis focuses on designing representation learning methods to improve the performance of dense prediction tasks in the field of medical image analysis. With advances in imaging technology, more complex medical images become available for use in this field. In contrast to traditional machine learning algorithms, current deep learning-based representation learning methods provide an end-to-end approach to automatically extract representations without the need for manual feature engineering from the complex data. In the field of medical image analysis, there are three unique challenges requiring the design of advanced representation learning architectures, \ie, limited labeled medical images, overfitting with limited data, and lack of interpretability. To address these challenges, we aim to design robust representation learning architectures for the two main directions of dense prediction tasks, namely medical image generation and segmentation. For medical image generation, the specific topic that we focus on is chromosome straightening. This task involves generating a straightened chromosome image from a curved chromosome input. In addition, the challenges of this task include insufficient training images and corresponding ground truth, as well as the non-rigid nature of chromosomes, leading to distorted details and shapes after straightening. We first propose a study for the chromosome straightening task. We introduce a novel framework using image-to-image translation and demonstrate its efficacy and robustness in generating straightened chromosomes. The framework addresses the challenges of limited training data and outperforms existing studies. We then present a subsequent study to address the limitations of our previous framework, resulting in new state-of-the-art performance and better interpretability and generalization capability. We propose a new robust chromosome straightening framework, named Vit-Patch GAN, which instead learns the motion representation of chromosomes for straightening while retaining more details of shape and banding patterns. For medical image segmentation, we focus on the fovea localization task, which is transferred from localization to small region segmentation. Accurate segmentation of the fovea region is crucial for monitoring and analyzing retinal diseases to prevent irreversible vision loss. This task also requires the incorporation of global features to effectively identify the fovea region and overcome hard cases associated with retinal diseases and non-standard fovea locations. We first propose a novel two-branch architecture, Bilateral-ViT, for fovea localization in retina image segmentation. This vision-transformer-based architecture incorporates global image context and blood vessel structure. It surpasses existing methods and achieves state-of-the-art results on two public datasets. We then propose a subsequent method to further improve the performance of fovea localization. We design a novel dual-stream deep learning architecture called Bilateral-Fuser. In contrast to our previous Bilateral-ViT, Bilateral-Fuser globally incorporates long-range connections from multiple cues, including fundus and vessel distribution. Moreover, with the newly designed Bilateral Token Incorporation module, Bilateral-Fuser learns anatomical-aware tokens, significantly reducing computational costs while achieving new state-of-the-art performance. Our comprehensive experiments also demonstrate that Bilateral-Fuser achieves better accuracy and robustness on both normal and diseased retina images, with excellent generalization capability

    On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

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    Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

    Deep Representation Learning with Limited Data for Biomedical Image Synthesis, Segmentation, and Detection

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    Biomedical imaging requires accurate expert annotation and interpretation that can aid medical staff and clinicians in automating differential diagnosis and solving underlying health conditions. With the advent of Deep learning, it has become a standard for reaching expert-level performance in non-invasive biomedical imaging tasks by training with large image datasets. However, with the need for large publicly available datasets, training a deep learning model to learn intrinsic representations becomes harder. Representation learning with limited data has introduced new learning techniques, such as Generative Adversarial Networks, Semi-supervised Learning, and Self-supervised Learning, that can be applied to various biomedical applications. For example, ophthalmologists use color funduscopy (CF) and fluorescein angiography (FA) to diagnose retinal degenerative diseases. However, fluorescein angiography requires injecting a dye, which can create adverse reactions in the patients. So, to alleviate this, a non-invasive technique needs to be developed that can translate fluorescein angiography from fundus images. Similarly, color funduscopy and optical coherence tomography (OCT) are also utilized to semantically segment the vasculature and fluid build-up in spatial and volumetric retinal imaging, which can help with the future prognosis of diseases. Although many automated techniques have been proposed for medical image segmentation, the main drawback is the model's precision in pixel-wise predictions. Another critical challenge in the biomedical imaging field is accurately segmenting and quantifying dynamic behaviors of calcium signals in cells. Calcium imaging is a widely utilized approach to studying subcellular calcium activity and cell function; however, large datasets have yielded a profound need for fast, accurate, and standardized analyses of calcium signals. For example, image sequences from calcium signals in colonic pacemaker cells ICC (Interstitial cells of Cajal) suffer from motion artifacts and high periodic and sensor noise, making it difficult to accurately segment and quantify calcium signal events. Moreover, it is time-consuming and tedious to annotate such a large volume of calcium image stacks or videos and extract their associated spatiotemporal maps. To address these problems, we propose various deep representation learning architectures that utilize limited labels and annotations to address the critical challenges in these biomedical applications. To this end, we detail our proposed semi-supervised, generative adversarial networks and transformer-based architectures for individual learning tasks such as retinal image-to-image translation, vessel and fluid segmentation from fundus and OCT images, breast micro-mass segmentation, and sub-cellular calcium events tracking from videos and spatiotemporal map quantification. We also illustrate two multi-modal multi-task learning frameworks with applications that can be extended to other domains of biomedical applications. The main idea is to incorporate each of these as individual modules to our proposed multi-modal frameworks to solve the existing challenges with 1) Fluorescein angiography synthesis, 2) Retinal vessel and fluid segmentation, 3) Breast micro-mass segmentation, and 4) Dynamic quantification of calcium imaging datasets

    Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

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    The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

    Cloud-based homomorphic encryption for privacy-preserving machine learning in clinical decision support

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    While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that ensures secure processing of sensitive data via untrusted networks in the public cloud or by third-party cloud vendors. It relies on the fact that some encryption algorithms display the property of homomorphism, which allows them to manipulate data meaningfully while still in encrypted form; although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. Such a framework would find particular relevance in Clinical Decision Support (CDS) applications deployed in the public cloud. CDS applications have an important computational and analytical role over confidential healthcare information with the aim of supporting decision-making in clinical practice. Machine Learning (ML) is employed in CDS applications that typically learn and can personalise actions based on individual behaviour. A relatively simple-to-implement, common and consistent framework is sought that can overcome most limitations of Fully Homomorphic Encryption (FHE) in order to offer an expanded and flexible set of HE capabilities. In the absence of a significant breakthrough in FHE efficiency and practical use, it would appear that a solution relying on client interactions is the best known entity for meeting the requirements of private CDS-based computation, so long as security is not significantly compromised. A hybrid solution is introduced, that intersperses limited two-party interactions amongst the main homomorphic computations, allowing exchange of both numerical and logical cryptographic contexts in addition to resolving other major FHE limitations. Interactions involve the use of client-based ciphertext decryptions blinded by data obfuscation techniques, to maintain privacy. This thesis explores the middle ground whereby HE schemes can provide improved and efficient arbitrary computational functionality over a significantly reduced two-party network interaction model involving data obfuscation techniques. This compromise allows for the powerful capabilities of HE to be leveraged, providing a more uniform, flexible and general approach to privacy-preserving system integration, which is suitable for cloud deployment. The proposed platform is uniquely designed to make HE more practical for mainstream clinical application use, equipped with a rich set of capabilities and potentially very complex depth of HE operations. Such a solution would be suitable for the long-term privacy preserving-processing requirements of a cloud-based CDS system, which would typically require complex combinatorial logic, workflow and ML capabilities

    Gaze-Based Human-Robot Interaction by the Brunswick Model

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    We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

    MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

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    Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

    Generalized averaged Gaussian quadrature and applications

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    A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

    SIS 2017. Statistics and Data Science: new challenges, new generations

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    The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data
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