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Shaker B, Lee J, Lee Y, Yu MS, Lee HM, Lee E, Kang HC, Oh KS, Kim HW, Na D. A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds. Bioinformatics 2023; 39:btad577. [PMID: 37713469 PMCID: PMC10560102 DOI: 10.1093/bioinformatics/btad577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/30/2023] [Accepted: 09/14/2023] [Indexed: 09/17/2023] Open
Abstract
MOTIVATION Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. RESULTS Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.
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Affiliation(s)
- Bilal Shaker
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Jingyu Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Yunhyeok Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Myeong-Sang Yu
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Hyang-Mi Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Eunee Lee
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children’s Hospital, Yonsei University College of Medicine, Epilepsy Research Institute, Seoul 03722, Republic of Korea
| | - Hoon-Chul Kang
- Department of Anatomy College of Medicine, Yonsei University, Seoul 03722, Republic of Korea
| | - Kwang-Seok Oh
- Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
| | - Hyung Wook Kim
- Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University, Seoul 05006, Republic of Korea
| | - Dokyun Na
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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Zemla K, Sedek G, Wróbel K, Postepski F, Wojcik GM. Investigating the Impact of Guided Imagery on Stress, Brain Functions, and Attention: A Randomized Trial. SENSORS (BASEL, SWITZERLAND) 2023; 23:6210. [PMID: 37448060 PMCID: PMC10346678 DOI: 10.3390/s23136210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023]
Abstract
The aim of this study was to investigate the potential impact of guided imagery (GI) on attentional control and cognitive performance and to explore the relationship between guided imagery, stress reduction, alpha brainwave activity, and attentional control using common cognitive performance tests. Executive function was assessed through the use of attentional control tests, including the anti-saccade, Stroop, and Go/No-go tasks. Participants underwent a guided imagery session while their brainwave activity was measured, followed by attentional control tests. The study's outcomes provide fresh insights into the influence of guided imagery on brain wave activity, particularly in terms of attentional control. The findings suggest that guided imagery has the potential to enhance attentional control by augmenting the alpha power and reducing stress levels. Given the limited existing research on the specific impact of guided imagery on attention control, the study's findings carry notable significance.
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Affiliation(s)
- Katarzyna Zemla
- Institute of Psychology, SWPS University of Social Sciences and Humanities, 03-815 Warsaw, Poland; (K.Z.)
| | - Grzegorz Sedek
- Institute of Psychology, SWPS University of Social Sciences and Humanities, 03-815 Warsaw, Poland; (K.Z.)
| | - Krzysztof Wróbel
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, 20-033 Lublin, Poland (F.P.)
| | - Filip Postepski
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, 20-033 Lublin, Poland (F.P.)
| | - Grzegorz M. Wojcik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, 20-033 Lublin, Poland (F.P.)
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Wojcik GM, Shriki O, Kwasniewicz L, Kawiak A, Ben-Horin Y, Furman S, Wróbel K, Bartosik B, Panas E. Investigating brain cortical activity in patients with post-COVID-19 brain fog. Front Neurosci 2023; 17:1019778. [PMID: 36845422 PMCID: PMC9947499 DOI: 10.3389/fnins.2023.1019778] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/12/2023] [Indexed: 02/11/2023] Open
Abstract
Brain fog is a kind of mental problem, similar to chronic fatigue syndrome, and appears about 3 months after the infection with COVID-19 and lasts up to 9 months. The maximum magnitude of the third wave of COVID-19 in Poland was in April 2021. The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis of the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B), and the control group that had no COVID-19 and no symptoms (sub-cohort C). The aim of this article was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and, if possible differentiate and classify them using the machine-learning tools. he dense array electroencephalographic amplifier with 256 electrodes was used for recordings. The event-related potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: face recognition, digit span, and task switching. These potentials were plotted for all three patients' sub-cohorts and all three experiments. The cross-correlation method was used to find differences, and, in fact, such differences manifested themselves in the shape of event-related potentials on the cognitive electrodes. The discussion of such differences will be presented; however, an explanation of such differences would require the recruitment of a much larger cohort. In the classification problem, the avalanche analysis for feature extractions from the resting state signal and linear discriminant analysis for classification were used. The differences between sub-cohorts in such signals were expected to be found. Machine-learning tools were used, as finding the differences with eyes seemed impossible. Indeed, the A&B vs. C, B&C vs. A, A vs. B, A vs. C, and B vs. C classification tasks were performed, and the efficiency of around 60-70% was achieved. In future, probably there will be pandemics again due to the imbalance in the natural environment, resulting in the decreasing number of species, temperature increase, and climate change-generated migrations. The research can help to predict brain fog after the COVID-19 recovery and prepare the patients for better convalescence. Shortening the time of brain fog recovery will be beneficial not only for the patients but also for social conditions.
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Affiliation(s)
- Grzegorz M. Wojcik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland,*Correspondence: Grzegorz M. Wojcik ✉
| | - Oren Shriki
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel,Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lukasz Kwasniewicz
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Andrzej Kawiak
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Yarden Ben-Horin
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Sagi Furman
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Krzysztof Wróbel
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Bernadetta Bartosik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Ewelina Panas
- Department of International Relations, Faculty of Political Science and Journalism, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
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Welcome to Bio-Algorithms and Med-Systems (BAMS). BIO-ALGORITHMS AND MED-SYSTEMS 2022. [DOI: 10.2478/bioal-2022-0092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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5
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Gonciarz W, Lechowicz Ł, Urbaniak M, Rechciński T, Chałubiński M, Broncel M, Kaca W, Chmiela M. Searching for serum biomarkers linking coronary heart disease and Helicobacter pylori infection using infrared spectroscopy and artificial neural networks. Sci Rep 2022; 12:18284. [PMID: 36316430 PMCID: PMC9622908 DOI: 10.1038/s41598-022-23191-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 10/26/2022] [Indexed: 11/07/2022] Open
Abstract
Helicobacter pylori (Hp) Gram-negative bacteria cause gastritis or gastric ulcers. They may be involved in the development of systemic diseases i.e. coronary heart disease (CHD). Both Hp infection and CHD are related to inflammation accompanied by C-reactive protein (CRP), tumor necrosis factor alfa (TNF-α) and homocysteine. Low density lipoprotein (LDL) and triglicerides are a classic risk factors of CHD. Infrared spectroscopy has been introduced for monitoring chronic infections or endogenous disorders using specific absorption bands for biocomponents typed as diagnostic markers. In this study we selected specific motives of infrared radiation (IR) spectra for the sera from CHD patients infected with Hp. In total 141 sera were used: 90 from patients with CHD, all Hp positive, and 51 from healthy donors, 32 Hp negative and 21 Hp positive. Hp status was evaluated by anti-Hp IgG antibodies and/or 13C urea breath testing. IR spectra were measured using FT-IR/FT-NIR Spectrum 400 spectrometer (PerkinElmer) chemometrically analyzed using artificial neural networks and they showed differences in absorption bands corresponding to triglicerides, CRP, homocysteine, LDL and TNF-α, and selected component groups between CHD patients infected with Hp vs healthy uninfected donors (96.15% accuracy). Triglicerides and CRP were the best biomarkers linking Hp infection with CHD.
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Affiliation(s)
- Weronika Gonciarz
- grid.10789.370000 0000 9730 2769Department of Immunology and Infectious Biology, Faculty of Biology and Environmental Protection, University of Lodz, Banacha 12/16, 90-237 Lodz, Poland ,grid.411821.f0000 0001 2292 9126Department of Synthesis and Structural Research, Faculty of Natural Sciences, Jan Kochanowski University, Świętokrzyska 11, 25-406 Kielce, Poland
| | - Łukasz Lechowicz
- grid.411821.f0000 0001 2292 9126Departament of Microbiology, Faculty of Natural Sciences, Jan Kochanowski University, Świętokrzyska 11, 25-406 Kielce, Poland
| | - Mariusz Urbaniak
- grid.411821.f0000 0001 2292 9126Department of Synthesis and Structural Research, Faculty of Natural Sciences, Jan Kochanowski University, Świętokrzyska 11, 25-406 Kielce, Poland
| | - Tomasz Rechciński
- grid.8267.b0000 0001 2165 3025Clinic and Department of Cardiology, Medical University of Lodz, 92-213 Lodz, Poland
| | - Maciej Chałubiński
- grid.8267.b0000 0001 2165 3025Department of Immunology and Allergy, Medical University of Lodz, Pomorska 251, 91-347 Lodz, Poland
| | - Marlena Broncel
- grid.8267.b0000 0001 2165 3025Laboratory of Tissue Immunopharmacology, Department of Internal Diseases and Clinical Pharmacology, Medical University of Lodz, Kniaziewicza 1/5, 91-347 Lodz, Poland
| | - Wiesław Kaca
- grid.411821.f0000 0001 2292 9126Departament of Microbiology, Faculty of Natural Sciences, Jan Kochanowski University, Świętokrzyska 11, 25-406 Kielce, Poland
| | - Magdalena Chmiela
- grid.10789.370000 0000 9730 2769Department of Immunology and Infectious Biology, Faculty of Biology and Environmental Protection, University of Lodz, Banacha 12/16, 90-237 Lodz, Poland
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Parra LA, Díaz DEM, Ramos F. Computational framework of the visual sensory system based on neuroscientific evidence of the ventral pathway. COGN SYST RES 2022. [DOI: 10.1016/j.cogsys.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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7
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Brodzicki A, Piekarski M, Jaworek-Korjakowska J. The Whale Optimization Algorithm Approach for Deep Neural Networks. SENSORS 2021; 21:s21238003. [PMID: 34884004 PMCID: PMC8659805 DOI: 10.3390/s21238003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 11/26/2021] [Accepted: 11/28/2021] [Indexed: 11/22/2022]
Abstract
One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many challenges including local minima, saddle points, and vanishing gradients. In this paper, we introduce the Whale Optimisation Algorithm (WOA) based on the swarm foraging behavior of humpback whales to optimise neural network hyperparameters. We wish to stress that to the best of our knowledge this is the first attempt that uses Whale Optimisation Algorithm for the optimisation task of hyperparameters. After a detailed description of the WOA algorithm we formulate and explain the application in deep learning, present the implementation, and compare the proposed algorithm with other well-known algorithms including widely used Grid and Random Search methods. Additionally, we have implemented a third dimension feature analysis to the original WOA algorithm to utilize 3D search space (3D-WOA). Simulations show that the proposed algorithm can be successfully used for hyperparameters optimization, achieving accuracy of 89.85% and 80.60% for Fashion MNIST and Reuters datasets, respectively.
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Hammad M, Pławiak P, Wang K, Acharya UR. ResNet‐Attention model for human authentication using ECG signals. EXPERT SYSTEMS 2021; 38. [DOI: 10.1111/exsy.12547] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 02/17/2020] [Indexed: 07/23/2024]
Abstract
AbstractAuthentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end‐to‐end deep neural network models for ECG‐based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet‐Attention is designed for human authentication. We have used 2‐s duration ECG signals obtained from two ECG databases (Physikalisch‐Technische Bundesanstalt [PTB] and Check Your Bio‐signals Here initiative [CYBHi]) for authentication. Our proposed ResNet‐Attention algorithm achieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real‐time authentication systems after the validation with more diverse ECG data.
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Affiliation(s)
- Mohamed Hammad
- School of Computer Science and Technology Harbin Institute of Technology Harbin China
- Faculty of Computers and Information Menoufia University Menoufia Egypt
| | - Paweł Pławiak
- Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland
- Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland
| | - Kuanquan Wang
- School of Computer Science and Technology Harbin Institute of Technology Harbin China
| | - Udyavara Rajendra Acharya
- Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore
- Department of Biomedical Engineering, School of Science and Technology Singapore School of Social Sciences Singapore Singapore
- Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan
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Transmission Quality Classification with Use of Fusion of Neural Network and Genetic Algorithm in Pay&Require Multi-Agent Managed Network. SENSORS 2021; 21:s21124090. [PMID: 34198587 PMCID: PMC8231990 DOI: 10.3390/s21124090] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/14/2021] [Accepted: 06/11/2021] [Indexed: 11/21/2022]
Abstract
Modern computer systems practically cannot function without a computer network. New concepts of data transmission are emerging, e.g., programmable networks. However, the development of computer networks entails the need for development in one more aspect, i.e., the quality of the data transmission through the network. The data transmission quality can be described using parameters, i.e., delay, bandwidth, packet loss ratio and jitter. On the basis of the obtained values, specialists are able to state how measured parameters impact on the overall quality of the provided service. Unfortunately, for a non-expert user, understanding of these parameters can be too complex. Hence, the problem of translation of the parameters describing the transmission quality appears understandable to the user. This article presents the concept of using Machine Learning (ML) to solve the above-mentioned problem, i.e., a dynamic classification of the measured parameters describing the transmission quality in a certain scale. Thanks to this approach, describing the quality will become less complex and more understandable for the user. To date, some studies have been conducted. Therefore, it was decided to use different approaches, i.e., fusion of a neural network (NN) and a genetic algorithm (GA). GA’s were choosen for the selection of weights replacing the classic gradient descent algorithm. For learning purposes, 100 samples were obtained, each of which was described by four features and the label, which describes the quality. In the reasearch carried out so far, single classifiers and ensemble learning have been used. The current result compared to the previous ones is better. A relatively high quality of the classification was obtained when we have used 10-fold stratified cross-validation, i.e., SEN = 95% (overall accuracy). The incorrect classification was 5/100, which is a better result compared to previous studies.
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Pałka F, Książek W, Pławiak P, Romaszewski M, Książek K. Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios. SENSORS 2021; 21:s21072293. [PMID: 33805937 PMCID: PMC8037346 DOI: 10.3390/s21072293] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/22/2021] [Accepted: 03/23/2021] [Indexed: 01/10/2023]
Abstract
This study is focused on applying genetic algorithms (GAs) to model and band selection in hyperspectral image classification. We use a forensic-inspired data set of seven hyperspectral images with blood and five visually similar substances to test GA-optimised classifiers in two scenarios: when the training and test data come from the same image and when they come from different images, which is a more challenging task due to significant spectral differences. In our experiments, we compare GA with a classic model optimisation through a grid search. Our results show that GA-based model optimisation can reduce the number of bands and create an accurate classifier that outperforms the GS-based reference models, provided that, during model optimisation, it has access to examples similar to test data. We illustrate this with experiments highlighting the importance of a validation set.
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Affiliation(s)
- Filip Pałka
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, 31-155 Krakow, Poland; (F.P.); (W.K.)
| | - Wojciech Książek
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, 31-155 Krakow, Poland; (F.P.); (W.K.)
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, 31-155 Krakow, Poland; (F.P.); (W.K.)
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland; (M.R.); or
- Correspondence: or
| | - Michał Romaszewski
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland; (M.R.); or
| | - Kamil Książek
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland; (M.R.); or
- Department of Data Sciences and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
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Kucharski D, Kleczek P, Jaworek-Korjakowska J, Dyduch G, Gorgon M. Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders. SENSORS 2020; 20:s20061546. [PMID: 32168748 PMCID: PMC7146382 DOI: 10.3390/s20061546] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 02/28/2020] [Accepted: 03/04/2020] [Indexed: 11/24/2022]
Abstract
In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the detection of nests of nevus cells in histopathological images of skin specimens is an important step in dermatopathology. The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, in order to distinguish between benign and malignant skin lesions. However, to the best of our knowledge, it is the first described method to segment the nests region. The novelty of our approach is not only the area of research, but, furthermore, we address a problem with a small ground-truth dataset. We propose an effective computer-vision based deep learning tool that can perform the nests segmentation based on an autoencoder architecture with two learning steps. Experimental results verified the effectiveness of the proposed approach and its ability to segment nests areas with Dice similarity coefficient 0.81, sensitivity 0.76, and specificity 0.94, which is a state-of-the-art result.
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Affiliation(s)
- Dariusz Kucharski
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
- Correspondence:
| | - Pawel Kleczek
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
| | - Joanna Jaworek-Korjakowska
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
| | - Grzegorz Dyduch
- Chair of Pathomorphology, Jagiellonian University Medical College, ul. Grzegorzecka 16, 31-531 Krakow, Poland
| | - Marek Gorgon
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
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Kleczek P, Jaworek-Korjakowska J, Gorgon M. A novel method for tissue segmentation in high-resolution H&E-stained histopathological whole-slide images. Comput Med Imaging Graph 2019; 79:101686. [PMID: 31816574 DOI: 10.1016/j.compmedimag.2019.101686] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 10/28/2019] [Accepted: 11/14/2019] [Indexed: 10/25/2022]
Abstract
Tissue segmentation in whole-slide images is an important task in digital pathology, required for efficient and accurate computer-aided diagnostics. Precise tissue segmentation is particularly significant for a correct diagnosis in cases, when tissue structure of a specimen is very porous, such as skin specimens. In this paper, we addressed the problem of fore- and background segmentation in histopatological images of skin specimens stained with hematoxylin and eosin (H&E), which has not been solved yet, by a novel method based on a combination of statistical analysis, color thresholding, and binary morphology. We validated our algorithm on large extracts from 60 high-resolution whole slide images, with differing staining quality and captured under varying imaging conditions, from three laboratories. The size of extracts varies from 2000×1500 to 20000×30000 pixels and the number of images used in our study matches the number of H&E images used by other research teams. We compared our method to the published ones (GrabCut and FESI) and showed that our approach outperforms its counterparts (Jaccard index of 0.929 vs. 0.776 and 0.695).
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Affiliation(s)
- Pawel Kleczek
- AGH University of Science and Technology, Department of Automatic Control and Robotics, Krakow, Poland.
| | | | - Marek Gorgon
- AGH University of Science and Technology, Department of Automatic Control and Robotics, Krakow, Poland
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13
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Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method. SENSORS 2019; 19:s19235079. [PMID: 31766323 PMCID: PMC6928852 DOI: 10.3390/s19235079] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 11/10/2019] [Accepted: 11/15/2019] [Indexed: 11/16/2022]
Abstract
Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.
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Kmiecik B, Łabowska M, Detyna J. Determination of the difference between two complex polymer models simulating the behaviour of biological structures. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.04.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Using Artificial Neural Networks to Solve the Problem Represented by BOD and DO Indicators. WATER 2017. [DOI: 10.3390/w10010004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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