Кафедра комп’ютерних наук

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https://elar.khmnu.edu.ua/handle/123456789/582

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Перегляд Кафедра комп’ютерних наук за Автор "Barmak, Oleksander"

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  • Ескіз
    Документ
    Explainable Artificial Intelligence: Transitioning DL Model Decisions to User-Understandable Features in Healthcare
    (CEUR-WS.org, 2024-10-05) Radiuk, Pavlo; Barmak, Oleksander; Manziuk, Eduard; Krak, Iurii
    Modern artificial intelligence (AI) solutions often face problems of the “black box” nature of deep learning (DL) models, which limits their transparency and trustworthiness in critical medical applications. In this study, we propose and evaluate a scalable approach for enhancing the interpretability of DL models in medical signal and image processing by translating complex model decisions into features that are understandable to healthcare professionals. The proposed approach was tested on two medical datasets: ECG signals for arrhythmia detection and MRI scans for heart disease classification. The performance of the DL models was compared with expert annotations, using Cohen’s Kappa coefficient as the primary metric to assess agreement. The study found strong agreement between DL model predictions and expert annotations, with Cohen’s Kappa coefficients of 0.89 for the ECG dataset and 0.80 for the MRI dataset, demonstrating the usefulness of the approach in providing reliable and interpretable results. In sum, the proposed scalable approach significantly improves the interpretability of DL models in medical applications.
  • Ескіз
    Документ
    Human-in-the-Loop Approach Based on MRI and ECG for Healthcare Diagnosis
    (CEUR-WS, 2022-12-14) Radiuk, Pavlo; Kovalchuk, Oleksii; Slobodzian, Vitalii; Manziuk, Eduard; Barmak, Oleksander; Krak, Iurii
    The presented study investigates a human-centric approach to implementing human-in-the-loop models for healthcare diagnostics. The following tasks were considered and addressed in this work: a) identify the features necessary for future healthcare diagnosis based on electrocardiogram signals in the human-in-the-loop model: P, T-peaks, QRS-complex, PQ and ST segments, and b) detect inflammatory processes in the heart muscle (myocardium) based on cardiac magnetic resonance imaging. As a result of our investigation, a novel approach was proposed for embedding (integrating) clinical knowledge about the nature of these phenomena into the electrocardiogram signal and magnetic resonance imaging. Domain knowledge about the sample’s nature is encoded similarly to the input information. Moreover, the convolution operation within our approach serves as an embedding mechanism. The results presented in the article are a starting point for using the models obtained by the proposed approach (human-in-the-loop models) for classification problems using deep learning and convolutional neural networks. Also, visual analysis shows the proposed approaches’ ability to solve practical clinical problems. It also ensures transparent interpretation of the obtained results as the human-in-the-loop model, which, in turn, is built according to the human-centric approach. Overall, our contribution allows the implementation of a scheme for obtaining artificial intelligence solutions based on the principles of trust in them.
  • Ескіз
    Документ
    Information technology for early diagnosis of pneumonia on individual radiographs
    (CEUR-WS, 2020-12-01) Krak, Iurii; Barmak, Oleksander; Radiuk, Pavlo
    Nowadays, pneumonia remains a disease with one of the highest death rates around the world. The ailment’s pathogen instantly causes a large amount of fluid into the lungs, leading to acute exacerbation. Without preliminary examination and timely treatment, pneumonia can result in severe pulmonary complications. Consequently, early diagnosis of pneumonia becomes a decisive factor in treatment and monitoring the disease. Therefore, information systems that can identify early pneumonia on the Chest X-ray images are becoming more demanding nowadays. An individual approach to a person might be a promising way of early diagnosis. The presented study considers an approach to feature extraction of the early stage of pneumonia and identifying the disease using a relatively simple convolutional neural network. With only three convolutional and two linearization layers, the proposed architecture classifies radiographs with 90.87% accuracy, approaching the results of deep multilayer and resource-intensive architectures in classification accuracy and exceeding them in time efficiency. Our approach requires relatively fewer computing resources, confirming its efficiency in solving practical tasks on available computing devices.
  • Ескіз
    Документ
    Integration of Contextual Descriptors in Ontology Alignment for Enrichment of Semantic Correspondence
    (IEEE, Inc., 2024-12-02) Manziuk, Eduard; Barmak, Oleksander; Radiuk, Pavlo; Kuznetsov, Vladislav; Krak, Iurii; Yakovlev, Sergiy
    This paper proposes a novel approach to semantic ontology alignment using contextual descriptors. A formalization was developed that enables the integration of essential and contextual descriptors to create a comprehensive knowledge model. The hierarchical structure of the semantic approach and the mathematical apparatus for analyzing potential conflicts between concepts, particularly in the example of "Transparency" and "Privacy" in the context of artificial intelligence, are demonstrated. Experimental studies showed a significant improvement in ontology alignment metrics after the implementation of contextual descriptors, especially in the areas of privacy, responsibility, and freedom & autonomy. The application of contextual descriptors achieved an average overall improvement of approximately 4.36%. The results indicate the effectiveness of the proposed approach for more accurately reflecting the complexity of knowledge and its contextual dependence.
  • Ескіз
    Документ
    Toward explainable deep learning in healthcare through transition matrix and user-friendly features
    (Frontiers Media SA, 2024-11-25) Barmak, Oleksander; Krak, Iurii; Yakovlev, Sergiy; Manziuk, Eduard; Radiuk, Pavlo; Kuznetsov, Vladislav
    Modern artificial intelligence (AI) solutions often face challenges due to the “black box” nature of deep learning (DL) models, which limits their transparency and trustworthiness in critical medical applications. In this study, we propose and evaluate a scalable approach based on a transition matrix to enhance the interpretability of DL models in medical signal and image processing by translating complex model decisions into user-friendly and justifiable features for healthcare professionals. The criteria for choosing interpretable features were clearly defined, incorporating clinical guidelines and expert rules to align model outputs with established medical standards. The proposed approach was tested on two medical datasets: electrocardiography (ECG) for arrhythmia detection and magnetic resonance imaging (MRI) for heart disease classification. The performance of the DL models was compared with expert annotations using Cohen’s Kappa coefficient to assess agreement, achieving coefficients of 0.89 for the ECG dataset and 0.80 for the MRI dataset. These results demonstrate strong agreement, underscoring the reliability of the approach in providing accurate, understandable, and justifiable explanations of DL model decisions. The scalability of the approach suggests its potential applicability across various medical domains, enhancing the generalizability and utility of DL models in healthcare while addressing practical challenges and ethical considerations.