Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635204
A. M. Samanovic, M. Nikolić, V. Zivkovic, Zeljko Mijailovic, A. Jevtic, M. Andjic, N. Draginic, Maja Savić, S. Bolevich, V. Jakovljevic
Introduction: Sepsis is a life-threatening condition characterized by organ dysfunction evoked by an abnormal host response to an infectious process. Statins, a class of lipid-lowering agents, possess immunomodulatory effects. Having in mind the potential benefit of treatment with statins in sepsis, we aimed to investigate the effects of statins on cardiac function and cardiac oxidative stress (OS) in the experimental sepsis rat model. Methodology: Thirty Wistar Albino rats (males, 8-weeks old, BW were randomly divided into 2 groups: Control group (C,) and animals with induced sepsis (SEPSIS,). After induced sepsis, all animals from the SEPSIS group were randomly divided into 4 subgroups: animals with induced sepsis without treatment (S), animals with induced sepsis treated with a single dose of Atorvastatin, Simvastatin, and Rosuvastatin. After 72h from sepsis induction, all animals were sacrificed and hearts were isolated and perfused according to the Langendorff technique gradually increasing coronary perfusion pressures 40–120 cmH20. In coronary venous effluent were determined the biomarkers of cardiac OS. All data were analyzed by one-way Anova and Kruskal-Wallis tests $(p < 0.05)$. Results: There were no changes in cardiodynamic parameters in septic rats without treatment, as well in those treated with statins, except for coronary flow, where its values were statistically increased in the sepsis group compared to all other groups. Also, sepsis was associated with disturbed cardiac OS and most of the applied therapeutic protocols of statins have mitigated the release of hydrogen peroxide and lipid peroxidation index, while in the case of superoxide anion radical, only atorvastatin at CPP 120 cmH2O had such a positive effect. Conclusion: Given the conditions of the current experiment, we can generally conclude that the rat's hearts in septic conditions were exposed to elevated OS which was not related to its functional changes. Additionally, statin therapy has achieved positive effects in terms of reduced release of molecules that could cause oxidative damage to the rat heart.
{"title":"The Effects of Statins on Cardiac Function and Oxidative Status in Rats with Sepsis","authors":"A. M. Samanovic, M. Nikolić, V. Zivkovic, Zeljko Mijailovic, A. Jevtic, M. Andjic, N. Draginic, Maja Savić, S. Bolevich, V. Jakovljevic","doi":"10.1109/BIBE52308.2021.9635204","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635204","url":null,"abstract":"Introduction: Sepsis is a life-threatening condition characterized by organ dysfunction evoked by an abnormal host response to an infectious process. Statins, a class of lipid-lowering agents, possess immunomodulatory effects. Having in mind the potential benefit of treatment with statins in sepsis, we aimed to investigate the effects of statins on cardiac function and cardiac oxidative stress (OS) in the experimental sepsis rat model. Methodology: Thirty Wistar Albino rats (males, 8-weeks old, BW were randomly divided into 2 groups: Control group (C,) and animals with induced sepsis (SEPSIS,). After induced sepsis, all animals from the SEPSIS group were randomly divided into 4 subgroups: animals with induced sepsis without treatment (S), animals with induced sepsis treated with a single dose of Atorvastatin, Simvastatin, and Rosuvastatin. After 72h from sepsis induction, all animals were sacrificed and hearts were isolated and perfused according to the Langendorff technique gradually increasing coronary perfusion pressures 40–120 cmH20. In coronary venous effluent were determined the biomarkers of cardiac OS. All data were analyzed by one-way Anova and Kruskal-Wallis tests $(p < 0.05)$. Results: There were no changes in cardiodynamic parameters in septic rats without treatment, as well in those treated with statins, except for coronary flow, where its values were statistically increased in the sepsis group compared to all other groups. Also, sepsis was associated with disturbed cardiac OS and most of the applied therapeutic protocols of statins have mitigated the release of hydrogen peroxide and lipid peroxidation index, while in the case of superoxide anion radical, only atorvastatin at CPP 120 cmH2O had such a positive effect. Conclusion: Given the conditions of the current experiment, we can generally conclude that the rat's hearts in septic conditions were exposed to elevated OS which was not related to its functional changes. Additionally, statin therapy has achieved positive effects in terms of reduced release of molecules that could cause oxidative damage to the rat heart.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115826568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635417
M. Milošević, B. Milićević, V. Simić, Vladimir Geroski, N. Filipovic, M. Kojic
The heart is a complex organ which produces mechanical force needed for the blood flow. Electrical signals are transformed into active stresses which contract the heart muscle and pump the blood out from the left ventricle. Therefore, comprehensive numerical procedure has to be established in order to simulate this process and to investigate the effects of different drugs on heart behavior. We here present application of the finite element (FE) computational model for simulation of heart beat cycle of the parametric left ventricle model. We are using Hunter excitation model for active, and direct experimental constitute relations for passive mechanical stresses. Additionally, computational model includes hysteretic and compressible behavior according to the experimental investigations. Applicability of our computational model is demonstrated using parametric left ventricle model which includes inlet mitral and outlet aortic valve cross-sections. With using different boundary conditions and prescribed values, this model has potential to mimic the effects of different drugs on heart beat cycle.
{"title":"Computational model for simulation of left ventricle behaviour during heart beat","authors":"M. Milošević, B. Milićević, V. Simić, Vladimir Geroski, N. Filipovic, M. Kojic","doi":"10.1109/BIBE52308.2021.9635417","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635417","url":null,"abstract":"The heart is a complex organ which produces mechanical force needed for the blood flow. Electrical signals are transformed into active stresses which contract the heart muscle and pump the blood out from the left ventricle. Therefore, comprehensive numerical procedure has to be established in order to simulate this process and to investigate the effects of different drugs on heart behavior. We here present application of the finite element (FE) computational model for simulation of heart beat cycle of the parametric left ventricle model. We are using Hunter excitation model for active, and direct experimental constitute relations for passive mechanical stresses. Additionally, computational model includes hysteretic and compressible behavior according to the experimental investigations. Applicability of our computational model is demonstrated using parametric left ventricle model which includes inlet mitral and outlet aortic valve cross-sections. With using different boundary conditions and prescribed values, this model has potential to mimic the effects of different drugs on heart beat cycle.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126770856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635498
Nevena Milivojević, D. Caballero, M. Carvalho, Mihajlo Kokanovic, M. Zivanovic, N. Filipovic, R. L. Reis, J. Oliveira
Preclinical experimentation demands for highly reliable and physiologically-relevant systems capable of recapitulating the complex human physiology. Further technological advances are in great need for improving our understanding about critical biological processes involved in tissue development or cancer progression, and for the discovery and screening of novel pharmacological drugs. Traditional in vitro models, albeit widely employed, fail to reproduce the complexity of the native scenario. Similarly, in vivo animal models poorly mimic the human condition and they are ethically questionable. During the last two decades, a new paradigm in preclinical modelling has emerged aiming to solve the limitations of the previous methods. The combination of advanced tissue engineering, cell biology and nanotechnology, has resulted in the development of cutting-edge microfluidics-based models with an unprecedented ability to recreate the native habitat of cells within a microengineered chip. Among the diverse variety of micro- and bio- fabrication techniques, UV-photolithography and soft lithography are considered the gold-standard methods for the fabrication of microfluidic chips to their simplicity, versatility, and rapid prototyping. In this paper, we describe a protocol for the fabrication of a microfluidic chip by UV-photolithography and replica molding, and an example of its use in cell migration assays.
{"title":"A Microfludic Platform as An In Vitro Model for Biomedical Experimentation - A Cell Migration Study","authors":"Nevena Milivojević, D. Caballero, M. Carvalho, Mihajlo Kokanovic, M. Zivanovic, N. Filipovic, R. L. Reis, J. Oliveira","doi":"10.1109/BIBE52308.2021.9635498","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635498","url":null,"abstract":"Preclinical experimentation demands for highly reliable and physiologically-relevant systems capable of recapitulating the complex human physiology. Further technological advances are in great need for improving our understanding about critical biological processes involved in tissue development or cancer progression, and for the discovery and screening of novel pharmacological drugs. Traditional in vitro models, albeit widely employed, fail to reproduce the complexity of the native scenario. Similarly, in vivo animal models poorly mimic the human condition and they are ethically questionable. During the last two decades, a new paradigm in preclinical modelling has emerged aiming to solve the limitations of the previous methods. The combination of advanced tissue engineering, cell biology and nanotechnology, has resulted in the development of cutting-edge microfluidics-based models with an unprecedented ability to recreate the native habitat of cells within a microengineered chip. Among the diverse variety of micro- and bio- fabrication techniques, UV-photolithography and soft lithography are considered the gold-standard methods for the fabrication of microfluidic chips to their simplicity, versatility, and rapid prototyping. In this paper, we describe a protocol for the fabrication of a microfluidic chip by UV-photolithography and replica molding, and an example of its use in cell migration assays.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130349100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635446
I. Lorencin, Klara Smolić, Sandi Baressi Segota, N. Andelic, D. Štifanić, J. Musulin, D. Markić, J. Španjol, Z. Car
In this paper, an approach for urinary bladder cancer diagnosis from computer tomography (CT) images based on the application of convolutional neural networks (CNN) is presented. The image data set that consists of three main parts (frontal, horizontal, and sagittal plane) is used. In order to classify images, pre-defined CNN architectures are used. CNN performances are evaluated by using 5-fold cross-validation procedure that gives information about classification and generalization performances. From the presented results, it can be noticed that higher performances are achieved if more complex CNN architectures are used. Higher performances can be noticed regardless of a plane in which images are captured. An increase in performances can be noticed in both classification and generalization context.
{"title":"Utilization of Convolutional Neural Networks for Urinary Bladder Cancer Diagnosis Recognition From CT Imagery","authors":"I. Lorencin, Klara Smolić, Sandi Baressi Segota, N. Andelic, D. Štifanić, J. Musulin, D. Markić, J. Španjol, Z. Car","doi":"10.1109/BIBE52308.2021.9635446","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635446","url":null,"abstract":"In this paper, an approach for urinary bladder cancer diagnosis from computer tomography (CT) images based on the application of convolutional neural networks (CNN) is presented. The image data set that consists of three main parts (frontal, horizontal, and sagittal plane) is used. In order to classify images, pre-defined CNN architectures are used. CNN performances are evaluated by using 5-fold cross-validation procedure that gives information about classification and generalization performances. From the presented results, it can be noticed that higher performances are achieved if more complex CNN architectures are used. Higher performances can be noticed regardless of a plane in which images are captured. An increase in performances can be noticed in both classification and generalization context.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"64 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131218910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635099
Ljubica Milanovic, S. Milenkovic, N. Petrovic, N. Grujovic, V. Slavkovic, F. Živić
This paper presents short review of the OCT technology concepts, including the recently emerging low-cost OCT devices. Technology concept of Fourier-domain OCT, based on spectral interferometry, is presented: spectral-domain OCT (SD-OCT) and swept-source OCT (SS-OCT). Technical properties of the recently developed low-cost OCT solutions are reviewed. Advanced OCT measurements in clinical ophthalmology are discussed, with relevant case studies. Further research directions that consider AI-based methods in OCT imaging is briefly presented.
{"title":"Optical Coherence Tomography (OCT) Imaging Technology","authors":"Ljubica Milanovic, S. Milenkovic, N. Petrovic, N. Grujovic, V. Slavkovic, F. Živić","doi":"10.1109/BIBE52308.2021.9635099","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635099","url":null,"abstract":"This paper presents short review of the OCT technology concepts, including the recently emerging low-cost OCT devices. Technology concept of Fourier-domain OCT, based on spectral interferometry, is presented: spectral-domain OCT (SD-OCT) and swept-source OCT (SS-OCT). Technical properties of the recently developed low-cost OCT solutions are reviewed. Advanced OCT measurements in clinical ophthalmology are discussed, with relevant case studies. Further research directions that consider AI-based methods in OCT imaging is briefly presented.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128333891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635229
Chuang Li, N. Bourbakis
Lately several studies have shown that Autonomous Intelligent Robotic Wheelchairs (AIRW) have offered valuable assistance to people with mobility issues. The assistance offered by these AIRW to these people is categorized according to the needs of each person. One category of such assistance is the rehabilitation exercises. Nosokoma is an AIRW that can offer a set of simple rehabilitation assistances for elderly and people in need. In particular, it can assist a user to perform simple rehab exercises, like “get up”, “turn around”, and “sit down”. Thus, in this paper, we show a rehab method based on the exercises mentioned above and the way that AIRW could collect the data from these exercises. More specifically, pressure sensors are attached on AIRW robotic arms to properly measure the force applied by the human user on the robotic arms, thus AIRW can provide additional lifting assistance to human user for the successful and safe completion of the sequence of get-up and sit-down tasks.
{"title":"A Long short-term Memory Model based on a Robotic Wheelchair as a Rehabilitation Assistant","authors":"Chuang Li, N. Bourbakis","doi":"10.1109/BIBE52308.2021.9635229","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635229","url":null,"abstract":"Lately several studies have shown that Autonomous Intelligent Robotic Wheelchairs (AIRW) have offered valuable assistance to people with mobility issues. The assistance offered by these AIRW to these people is categorized according to the needs of each person. One category of such assistance is the rehabilitation exercises. Nosokoma is an AIRW that can offer a set of simple rehabilitation assistances for elderly and people in need. In particular, it can assist a user to perform simple rehab exercises, like “get up”, “turn around”, and “sit down”. Thus, in this paper, we show a rehab method based on the exercises mentioned above and the way that AIRW could collect the data from these exercises. More specifically, pressure sensors are attached on AIRW robotic arms to properly measure the force applied by the human user on the robotic arms, thus AIRW can provide additional lifting assistance to human user for the successful and safe completion of the sequence of get-up and sit-down tasks.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131986624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635501
Glykeria Sdoukopoulou, M. Antonakakis, Gabriel Modde, C. Wolters, M. Zervakis
Epilepsy is one of the most common brain disorders worldwide. The basic principle in epilepsy is to resect the epileptogenic zone (EZ) when the medicaments are inadequate to suppress epileptic seizures. Epilepsy is accompanied by interictal spikes, a surrogate marker serving as an identifier of seizures. The automatic temporal detection of these spikes is of major importance due to the demanding time consumption of the manual annotation. Electro- and magneto- encephalography (EEG and MEG) are the most usual measurement modalities for the recording of brain activity. EEG and MEG are ideal modalities for the non-invasive monitoring of drug-resistant epilepsy. Many approaches have been proposed for the temporal detection of interictal spikes. However, only single measurement modality (EEG or MEG) has been used up to now, neglecting their complementary content. In this study, we develop a multi-feature and iterative classification scheme with input from either single modality (EEG or MEG) or combined EEG/MEG (EMEG). The inputs include statistical (kurtosis and Renyi Entropy) and spectral (Energy) features as well as the functional connectivity metrics, global and local efficiency from imaginary phase lag index networks. The classification performance for all modalities ranges from 89% to 92.8%, with the maximum performance being observed for EMEG. Overall, the complementarity of EEG and MEG on the detection of interictal spikes is promising, opening new considerations on the development of automatic epileptic spike detection approaches.
{"title":"Interictal Spike Classification in Pharmacoresistant Epilepsy using Combined EEG and MEG","authors":"Glykeria Sdoukopoulou, M. Antonakakis, Gabriel Modde, C. Wolters, M. Zervakis","doi":"10.1109/BIBE52308.2021.9635501","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635501","url":null,"abstract":"Epilepsy is one of the most common brain disorders worldwide. The basic principle in epilepsy is to resect the epileptogenic zone (EZ) when the medicaments are inadequate to suppress epileptic seizures. Epilepsy is accompanied by interictal spikes, a surrogate marker serving as an identifier of seizures. The automatic temporal detection of these spikes is of major importance due to the demanding time consumption of the manual annotation. Electro- and magneto- encephalography (EEG and MEG) are the most usual measurement modalities for the recording of brain activity. EEG and MEG are ideal modalities for the non-invasive monitoring of drug-resistant epilepsy. Many approaches have been proposed for the temporal detection of interictal spikes. However, only single measurement modality (EEG or MEG) has been used up to now, neglecting their complementary content. In this study, we develop a multi-feature and iterative classification scheme with input from either single modality (EEG or MEG) or combined EEG/MEG (EMEG). The inputs include statistical (kurtosis and Renyi Entropy) and spectral (Energy) features as well as the functional connectivity metrics, global and local efficiency from imaginary phase lag index networks. The classification performance for all modalities ranges from 89% to 92.8%, with the maximum performance being observed for EMEG. Overall, the complementarity of EEG and MEG on the detection of interictal spikes is promising, opening new considerations on the development of automatic epileptic spike detection approaches.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125094606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635303
R. L. Kæseler, L. Struijk, M. Jochumsen
While assistive robotic devices can improve the quality of life for individuals with tetraplegia, it is difficult to provide a high-performing interface that can be fully utilized, with little to no motor functionality. While a brain-computer interface (BCI) can be used with little to no motor functionality, it typically has a low performance. Steady-state visually evoked potentials (SSVEP) provide some of the best performing signals for a BCI, but are rarely investigated for online asynchronous control where not only accuracy is important, but also the computational costs. This study investigates and compares three classifiers: the well-known and high-performing task-related component analysis (TRCA), the computational efficient Spatiotemporal beamformer (STBF) build on the stimulus-locked inter-trace correlation (SLIC) algorithm and our proposed novel algorithm which combines the two: the SLIC-TRCA. Results show the SLIC-TRCA achieving higher accuracies ${(95.00pm 5.36%}$ with a 1s classification window) compared to the TRCA ${(88.25pm 14.58%)}$ and similar compared to the STBF ${(91.00pm 11.02%)}$ while having a much lower computational cost (519% faster than the TRCA and 144% faster than the STBF). We, therefore, believe this algorithm has an exciting potential as it will allow a high classification accuracy without requiring a high-performing CPU.
{"title":"Optimizing steady-state visual evoked potential classifiers for high performance and low computational costs in brain-computer interfacing","authors":"R. L. Kæseler, L. Struijk, M. Jochumsen","doi":"10.1109/BIBE52308.2021.9635303","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635303","url":null,"abstract":"While assistive robotic devices can improve the quality of life for individuals with tetraplegia, it is difficult to provide a high-performing interface that can be fully utilized, with little to no motor functionality. While a brain-computer interface (BCI) can be used with little to no motor functionality, it typically has a low performance. Steady-state visually evoked potentials (SSVEP) provide some of the best performing signals for a BCI, but are rarely investigated for online asynchronous control where not only accuracy is important, but also the computational costs. This study investigates and compares three classifiers: the well-known and high-performing task-related component analysis (TRCA), the computational efficient Spatiotemporal beamformer (STBF) build on the stimulus-locked inter-trace correlation (SLIC) algorithm and our proposed novel algorithm which combines the two: the SLIC-TRCA. Results show the SLIC-TRCA achieving higher accuracies ${(95.00pm 5.36%}$ with a 1s classification window) compared to the TRCA ${(88.25pm 14.58%)}$ and similar compared to the STBF ${(91.00pm 11.02%)}$ while having a much lower computational cost (519% faster than the TRCA and 144% faster than the STBF). We, therefore, believe this algorithm has an exciting potential as it will allow a high classification accuracy without requiring a high-performing CPU.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116977730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635175
Lin Li, Wonseok Seo
According to Skin Cancer Foundation, skin cancer is by far the most common type of cancer in the United States and worldwide. Early diagnosis of skin cancer is critical because proper treatment at early stages can increase the chance of cure and recovery. However, visual inspection of dermoscopic images by dermatologists is error-prone and time-consuming. To ensure accurate diagnosis and faster treatment of skin cancer, deep learning techniques have been utilized to conduct automated skin lesion segmentation and classification. In this paper, after image processing, a Mask R-CNN model is built for lesion segmentation, where transfer learning is utilized by using the pre-trained weights from Microsoft COCO dataset. The weights of the trained Mask R-CNN model are saved and transferred to the next task - skin lesion classification, to train a Mask R-CNN model for classification. Our experiments are conducted on the benchmark datasets from the International Skin Imaging Collaboration 2018 (ISIC 2018) and evaluated by the same metrics used in ISIC 2018. The lesion boundary segmentation and lesion classification have achieved an accuracy of 96% and a balanced multiclass accuracy of 80%, respectively.
{"title":"Deep Learning and Transfer Learning for Skin Cancer Segmentation and Classification","authors":"Lin Li, Wonseok Seo","doi":"10.1109/BIBE52308.2021.9635175","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635175","url":null,"abstract":"According to Skin Cancer Foundation, skin cancer is by far the most common type of cancer in the United States and worldwide. Early diagnosis of skin cancer is critical because proper treatment at early stages can increase the chance of cure and recovery. However, visual inspection of dermoscopic images by dermatologists is error-prone and time-consuming. To ensure accurate diagnosis and faster treatment of skin cancer, deep learning techniques have been utilized to conduct automated skin lesion segmentation and classification. In this paper, after image processing, a Mask R-CNN model is built for lesion segmentation, where transfer learning is utilized by using the pre-trained weights from Microsoft COCO dataset. The weights of the trained Mask R-CNN model are saved and transferred to the next task - skin lesion classification, to train a Mask R-CNN model for classification. Our experiments are conducted on the benchmark datasets from the International Skin Imaging Collaboration 2018 (ISIC 2018) and evaluated by the same metrics used in ISIC 2018. The lesion boundary segmentation and lesion classification have achieved an accuracy of 96% and a balanced multiclass accuracy of 80%, respectively.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123052178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635499
Georgios Galanos, Pavlos Malakonakis, A. Dollas
Genome assembly is a field of bioinformatics which refers to the process of taking small fragments of genetic material and putting them back together in order to reconstruct the original DNA sequence from which the fragments originated. As the DNA genome assembly input datasets in most cases have a very large amount of data, it is important to develop custom architectures in order to speed up these processes and gain significant execution time reduction. In this paper we present the Reads Matching Filter (RMF), an input dataset prefiltering process, based on string matching and implemented on Field Programmable Gate Array (FPGA) technology, in order to reduce the genome assembly execution time. The outputs of the RMF running on the FPGA as well as the original input dataset are given as input to the Velvet genome assembler which produces the assembly of the input sequences. The Velvet genome assembler is based on the manipulation of de Bruijn graphs, and produces its output via the removal of errors and the simplication of repeated regions. The FPGA-based RMF pre-filtering process manages to speedup the entire genome assembly processing, including I/O, by up to 6 times, while maintaining the quality of the output sequence contigs (i.e. the series of overlapping DNA sequences).
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