Examinando por Autor "Sierra-Sosa, Daniel"

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    Deep learning models for colorectal polyps
    (MDPI AG, 2021-06-10) Bardhi, Ornela; Sierra-Sosa, Daniel; García-Zapirain, Begoña; Bujanda Fernández de Piérola, Luis
    Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to date
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    Diabetes type 2: poincaré data preprocessing for quantum machine learning
    (Tech Science Press, 2021-02-05) Sierra-Sosa, Daniel ; Arcila-Moreno, Juan D.; García-Zapirain, Begoña; Elmaghraby, Adel Said
    Quantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier (VQC), which development seems promising.Albeit being largely studied, VQC implementations for "real-world" datasets are still challenging on Noisy Intermediate Scale Quantum devices (NISQ). In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping. This pipeline enhances the prediction rates when applying VQC techniques, improving the feasibility of solving classification problems using NISQ devices. By including feature selection techniques and geometrical transformations, enhanced quantum state preparation is achieved.Also, a representation based on the Stokes parameters in the Poincare Sphere is possible for visualizing the data.Our results showthat by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients. We used the implemented version of VQC available on IBM s framework Qiskit, and obtained with two and three qubits an accuracy of 70% and 72% respectively.
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    Exploiting deep learning techniques for colon polyp segmentation
    (Tech Science Press, 2021-02-05) Sierra-Sosa, Daniel ; Patiño Barrientos, Sebastián; García-Zapirain, Begoña ; Castillo Olea, Cristian; Elmaghraby, Adel Said
    As colon cancer is among the top causes of death, there is a growing interest in developing improved techniques for the early detection of colon polyps. Given the close relation between colon polyps and colon cancer, their detection helps avoid cancer cases. The increment in the availability of colorectal screening tests and the number of colonoscopies have increased the burden on the medical personnel. In this article, the application of deep learning techniques for the detection and segmentation of colon polyps in colonoscopies is presented. Four techniques were implemented and evaluated: Mask-RCNN, PANet, Cascade R-CNN and Hybrid Task Cascade (HTC). These were trained and tested using CVC-Colon database, ETIS-LARIB Polyp, and a proprietary dataset. Three experiments were conducted to assess the techniques performance: 1) Training and testing using each database independently, 2) Mergingd the databases and testing on each database independently using a merged test set, and 3) Training on each dataset and testing on the merged test set. In our experiments, PANet architecture has the best performance in Polyp detection, and HTC was the most accurate to segment them. This approach allows us to employ Deep Learning techniques to assist healthcare professionals in the medical diagnosis for colon cancer. It is anticipated that this approach can be part of a framework for a semi-Automated polyp detection in colonoscopies.
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    Quantum machine learning applications in the biomedical domain: a systematic review
    (Institute of Electrical and Electronics Engineers Inc., 2022) Maheshwari, Danyal; García-Zapirain, Begoña; Sierra-Sosa, Daniel
    Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning has become quite a challenging field in the case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models and quantum circuits using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.
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    Scalable healthcare assessment for diabetic patients using deep learning on multiple GPUS
    (IEEE Computer Society, 2019-10) Sierra-Sosa, Daniel; García-Zapirain, Begoña; Castillo Olea, Cristian; Oleagordia Ruiz, Ibon; Nuño Solinís, Roberto; Urtaran Laresgoiti, Maider; Elmaghraby, Adel Said
    The large-scale parallel computation that became available on the new generation of graphics processing units (GPUs) and on cloud-based services can be exploited for use in healthcare data analysis. Furthermore, computation workstations suited for deep learning are usually equipped with multiple GPUs allowing for workload distribution among multiple GPUs for larger datasets while exploiting parallelism in each GPU. In this paper, we utilize distributed and parallel computation techniques to efficiently analyze healthcare data using deep learning techniques. We demonstrate the scalability and computational benefits of this approach with a case study of longitudinal assessment of approximately 150 000 type 2 diabetic patients. Type 2 diabetes mellitus (T2DM) is the fourth case of mortality worldwide with rising prevalence. T2DM leads to adverse events such as acute myocardial infarction, major amputations, and avoidable hospitalizations. This paper aims to establish a relation between laboratory and medical assessment variables with the occurrence of the aforementioned adverse events and its prediction using machine learning techniques. We use a raw database provided by Basque Health Service, Spain, to conduct this study. This database contains 150 156 patients diagnosed with T2DM, from whom 321 laboratory and medical assessment variables recorded over four years are available. Predictions of adverse events on T2DM patients using both classical machine learning and deep learning techniques were performed and evaluated using accuracy, precision, recall and F1-score as metrics. The best performance for the prediction of acute myocardial infarction is obtained by linear discriminant analysis (LDA) and support vector machines (SVM) both balanced and weight models with an accuracy of 97%; hospital admission for avoidable causes best performance is obtained by LDA balanced and SVMs balanced both with an accuracy of 92%. For the prediction of the incidence of at least one adverse event, the model with the best performance is the recurrent neural network trained with a balanced dataset with an accuracy of 94.6%. The ability to perform and compare these experiments was possible through the use of a workstation with multi-GPUs. This setup allows for scalability to larger datasets. Such models are also cloud ready and can be deployed on similar architectures hosted on AWS for even larger datasets.
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    Variational quantum classifier for binary classification: real vs synthetic dataset
    (Institute of Electrical and Electronics Engineers Inc., 2022) Maheshwari, Danyal; Sierra-Sosa, Daniel; García-Zapirain, Begoña
    Nowadays, quantum-enhanced methods have been widely studied to solve machine learning related problems. This article presents the application of a Variational Quantum Classifier (VQC) for binary classification. We utilized three datasets: a synthetic dataset with randomly generated values between 0 and 1, the publicly available University of California Intelligence Machine learning (UCI) sonar dataset consisting of mining data, and a proprietary diabetes dataset related to diabetes with acute diseases and diabetes without acute disease. To deal with the limitation of noisy intermediate-scale quantum systems (NISQ), we used a pre-processing method to enhance the prediction rate when applying the VQC method. The process includes feature selection and state preparation. Quantum state preparation is critical for obtaining a functioning pipeline in a quantum machine learning (QML) model. Amplitude encoding is a state preparation approach that enhances the performance of data encoding and the learning of quantum models. As a result, our proposed methods achieved accuracies of 75%, 71.4%, and 68.73% by using VQC model and in contrast, the amplitude encoding-based VQC achieved 98.40%, 67.3%, and 74.50% accuracies on the synthetic, sonar, and diabetes dataset, respectively.