AGH Reseach Team "ASSOCIATIVE BRAIN"

  • [Rozmiar: 11195 bajtów]
  • [Rozmiar: 11195 bajtów]
  • [Rozmiar: 11195 bajtów]

The team was founded in 2019 and dedicated to develop innovative models of neurons and their self-organizing structures capable of adapting to any data and relations between them, building associations and using them in cognitive processes, prediction, clustering, grouping, classification, inference, recommendation, modeling emotions and needs, learning with the use of motivation, knowledge formation and strong artificial intelligence, and their applications, inter alia, for the use of natural language and in various issues of biomedical engineering.

AGH RESEARCH TEAM:

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SCOPE OF RESEARCH AND DEVELOPMENT INTERESTS:

The ASSOCIATIVE BRAIN research team deals with the development of models, structures, algorithms, and solutions in the field of modern artificial intelligence, deep learning, knowledge engineering, and intelligent associative-cognitive systems based on knowledge formation to solve important tasks, among others in the field of computational linguistics, biomedical engineering and medicine. Our goal is to develop and improve methods, structures, algorithms, and solutions that support the work of engineers during the construction of complex systems and doctors in diagnostics, classification, prediction, detection, and recommendations, as well as gathering knowledge and optimizing methods of treating patients, as well as preventing and warning on knowledge base as well as predictive and recommending algorithms. The built systems and structures are designed to automatically adapt to data and the relations that connect them, to form knowledge about problems and, based on associations, search for solutions and remember them in neural structures.

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DESCRIPTION OF THE RESEARCH CONDUCTED IN THE FIELD OF BIOCYBERNETICS, KNOWLEDGE ENGINEERING, COGNITIVISTICS, AND STRONG ARTIFICIAL INTELLIGENCE (since 2019):

Modern artificial intelligence relies on machine learning to enable approximation and grouping through various models, including deep neural networks, which have many advantages and disadvantages. Unfortunately, modern solutions are still far away from reaching the level of worm intelligence, let alone human intelligence, despite many successes in the field of adaptation and machine learning. However, these successes are related mainly to only a few groups of solutions related to classification, clustering, and prediction (regression), which is only a very preliminary stage to the formation of true artificial intelligence. Of course, many research groups around the world are constantly striving to expand these boundaries and implement real artificial intelligence.

The research conducted within our group is aimed at broadening the knowledge about the processes of knowledge formation in the human mind and transferring it to the field of computer science. So far, computer science has mainly focused on data storage and processing, but knowledge results primarily from the relationship between data and its groups that model various objects and events. In artificial intelligence, too little attention has been paid to the reception processes themselves, without which the data would not reach the neural networks, and the intelligence would not be able to develop. An important element of any biological neural network is also the environment (cerebrospinal fluid) and the relative distances of the neurons, which are often neglected in neural network models. The lack of the needs and feelings of artificial systems does not allow for the creation of motivational feedback loops, so artificial systems do not have the motivation to expand knowledge in important aspects or to form artificial intelligence. In our team, we deal with these important aspects and develop very interesting, futuristic models that break down the barriers of human cognition, enabling the understanding of associative brain processes and transferring them to the field of computer science, creating knowledge-based models of artificial intelligence.


ANAKG - Active Neural Associative Knowledge Graphs
for sequential data training and semantic generalization

APNN - Associative Pulsing Neural Networks
the 4th generation of neural networks based on plastic associative pulsing neurons

DESCRIPTION OF THE RESEARCH CONDUCTED IN BIOMEDICINE (since 2020):

As the latest research shows (Eric Topol, "Deep Medicine. How Artificial Intelligence Can Make Healthcare Human Again", New York, 2019), up to 60% of commissioned medical tests and procedures are carried out incorrectly, inaccurately or unnecessarily due to limited time for subjective examination and subject, familiarization with the history of the disease and erroneous, fast, inaccurate or insufficient differential analysis and diagnosis. This often results in improper treatment that does not bring about improvement in health, deterioration of health, or even death. The most common cause of death in Poland is cardiovascular disease (43.5% in 2017 according to the CSO Demographic Yearbook), which due to the complicated diagnostic and therapeutic process, lack of proper preparation of some doctors participating in the treatment process, lack of access to appropriate diagnostic procedures, as well as the lack of time necessary for adequate analysis of available medical data, are often inadequately treated, causing the early threats to be overlooked, leading to further complications, heart failure, and even death.

Electrocardiography (ECG) is one of the most frequently performed tests in Poland. According to the CSO report "Health and health care in 2016," as much as 13.2% of the population had an ECG during the year as part of outpatient care. This is due to, among others, low cost of testing, availability of apparatus, relatively simple operation of the equipment, non-invasiveness, and high diagnostic value of the examination. Electrocardiography and its variants, i.e., Holter ECG monitoring (continuous recording of ECG signal for 24 hours or several days) or exercise ECG, are indicated, among others in patients with previously diagnosed cardiovascular disease, in people with suspected cardiovascular disease and healthy people with an increased risk of cardiovascular disease. Besides, imaging tests, i.e., echocardiography, computed tomography, X-ray, cardiac magnetic resonance imaging, and coronary angiography, play an important role in the diagnostic process of heart disease. Echocardiography is one of the most frequently performed outpatient care tests (4.8% of the population in 2016, according to the Central Statistical Office). For many years, attempts have been made to computer support the diagnostic process based on ECG signals and cardiac imaging. Many of them, despite promising results, have not been implemented into clinical practice. This could have been caused, among others, by lack of a comprehensive and practical approach to the developed solutions enabling easy implementation into clinical practice, insufficient effectiveness of these methods, or a high demand for computing power, which for financial and organizational reasons is often not available in clinical practice.

The process of recognizing patterns and then extracting the knowledge contained in ECG signals and imaging of the heart is certainly not a trivial task while remaining very important from the point of view of potential benefits. Many approaches to the analysis of ECG signals by classical methods and those using deep neural networks have failed, because due to their mode of operation, they take into account only a certain subset of essential features that they are not able to properly associate together, leading to misclassification. In addition, deep networks are insensitive to small changes, which in the case of an ECG signal are often of fundamental importance, indicating, e.g., symptoms of a heart attack or a specific type of arrhythmia. Another critical problem of deep neural networks is low energy efficiency - both learning and inference require equipment with enormous computing power, which in practice is often not available, thus limiting the possibilities of implementing these solutions in everyday clinical and academic practice. This problem particularly concerns the analysis of medical images, which due to the multidimensionality of data (from 1 to 3 dimensions of the geometric space, from 1 to 4 dimensions of the color space and time) require access to very efficient equipment to be able to analyze such data using the most effective learning methods deep.

The purpose of this research team's research is to build a new adaptive model of heart activity based on neural associative-cognitive models that can eliminate these inconveniences and develop a representation of ECG signals in an unprecedented manner taking into account the frequency and number of changes over time, using frequent events and rare at the same time. In addition, thanks to effective representation of knowledge in the associative-cognitive model, it will be possible to associate information obtained thanks to the analysis of other modalities using machine learning techniques, mainly cardiac imaging, i.e., echocardiography, computed tomography, X-ray, magnetic resonance imaging or coronary angiography. The research will also create new neural and associative data structures, algorithms, and computational methods that search for associatively aggregated frequent patterns, representing them in an associative model of knowledge and classifying cardiovascular diseases.

In addition, it is planned to use many existing, leading solutions based mainly on deep learning models (advanced models based on convolutional and recursive neural networks) that will support the associative-cognitive model in specific well-defined tasks, as well as serve as a reference point in the conducted tests.

During the research, models of associative knowledge representation for biomedical signals and images will be developed, developing the team's scientific workshop and enabling applications for external grants for research and implementation in medical equipment or the form of computer applications.

Research conducted as part of this research group falls under the priority research areas of artificial intelligence, knowledge engineering, cognitive science, biocybernetics, and biomedical engineering (POB 5) and includes the development of these methods to obtain automatic classification and support of doctors, and then use in telemedicine (POB 6), saving health and the lives of many sick people affected by heart disease.

A specialized computer equipped with modern GPGPU units, certified medical monitors that meet the requirements of the TK and MR diagnostic station (compliance with the DICOM standard), a modern ECG apparatus allowing to record signals in high quality and digital form and modern single- and multi-channel ECG holsters will be used in the research to perform calculations, embed created algorithms, test computational models, collect data, and evaluate possible applications in telemedicine.