GT10 Medical Imaging II

Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
Conference room
Session time
Saturday, June 24, 2017 - 14:45 to 15:45
A comparative study of cell nuclei attributed relational graphs for knowledge description and categorization in histopathological gastric cancer whole slide images
In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification.
Harshita Sharma's picture
Harshita Sharma
Technical University Berlin (DE)
Norman Zerbe's picture
Norman Zerbe
Christine BOger's picture
Christine BOger
Stephan Wienert's picture
Stephan Wienert
Olaf Hellwich's picture
Olaf Hellwich
Peter Hufnagl's picture
Peter Hufnagl
Non-Invasive Assessment of Coronary Stenoses and Comparison to Invasive Techniques: a proof-of-concept study
Coronary Computed Tomography Angiography (CCTA) has gained substantial ground in everyday clinical practice due to its non-invasive nature. In this work we present a noninvasive method to assess the hemodynamic significance of coronary stenoses using only CCTA images. Two female patients were subjected to Invasive Coronary Angiography, Virtual Histology IVUS and CCTA. The same arterial segment was reconstructed in 3D using the proposed method as well as two already validated 3D reconstruction methods using the aforementioned invasive techniques.
Panagiota Tsompou's picture
Panagiota Tsompou
Panagiotis Siogkas's picture
Panagiotis Siogkas
University of Ioannina (GR)
Antonis Sakellarios's picture
Antonis Sakellarios
Pedro Lemos's picture
Pedro Lemos
Lampros Michalis's picture
Lampros Michalis
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Automated collagen proportional area extraction in liver biopsy images using a novel classification via clustering algorithm
Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized.
Dimosthenis C. Tsouros's picture
Dimosthenis C. Tsouros
Panagiotis N. Smyrlis's picture
Panagiotis N. Smyrlis
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Pinelopi Manousou's picture
Pinelopi Manousou
Dimitrios G. Tsalikakis's picture
Dimitrios G. Tsalikakis
Markos G. Tsipouras's picture
Markos G. Tsipouras
University of Western Macedonia
A Deconstructed Replication of Time of Test Using the AGIS Metric (Skyline paper)
In medical practice, glaucoma severity is usually measured using the Advanced Glaucoma Intervention Studies (AGIS) metric. In a previous study, we replicated the work of Montolio et al.,and demonstrated that, for a larger dataset, time of day of test using the AGIS metric did make a difference to the measurement of glaucoma, supporting Montolio et al’s work. However, in our earlier study, we used the AGIS scores for both eyes combined. In this paper, we use the measurement from just one eye at a time.
Steve Counsell's picture
Steve Counsell
Stephen Swift's picture
Stephen Swift
Brunel University (UK)
Allan Tucker's picture
Allan Tucker
Brunel University London (UK)

ST2 - Social data and medical data analytics

Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Athena Vakali's picture
Athena Vakali
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 11:00
Mobile Crowdsensing for the Juxtaposition of Realtime Assessments and Retrospective Reporting for Neuropsychiatric Symptoms
Many symptoms of neuropsychiatric disorders such as tinnitus are subjective and variable over time. Typically, patients are asked to report symptoms, their severity, and duration retrospectively, e.g., in interviews or self-report questionnaires. However, little is known on how well such retrospective reports correspond with the experience of the symptoms at the moment they occurred in daily life. Mobile technologies can help in that end: mobile self-help services allow patients to record their symptoms prospectively, when (or short after) they occur in daily life.
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Thomas Probst's picture
Thomas Probst
Winfried Schlee's picture
Winfried Schlee
Johannes Schobel's picture
Johannes Schobel
Berthold Langguth's picture
Berthold Langguth
Patrick Neff's picture
Patrick Neff
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
Manfred Reichert's picture
Manfred Reichert
Mining Facebook data of people with rare diseases
"This research is concerned with the study of Spanish Facebook pages that deal with rare diseases. The objectives of this research are to characterise these pages and to compare them with the priorities of the Decalogue of the Spanish Federation of Rare Diseases (FEDER). This research uses Netvizz to download the data, word clouds in R to perform text mining, TextBlob in Python to perform sentiment analysis, and log-likelihood in R to compare Facebook and Decalogue words.
Natalia Reguera's picture
Natalia Reguera
Laia Subirats's picture
Laia Subirats
Eurecat & Open University of Catalonia (SP)
Manuel Armayones's picture
Manuel Armayones
OncoCall: analyzing the outcomes of the oncology telephone patient assistance
Hospital Puerta del Hierro in Madrid, Spain, implemented in November 2011 a new service that aims to aid the patients of the oncology service with their doubts during their treatments through the use of a centralized call center. This service was created with the aim of provide a more personalized patient attention as well as to try to reduce the number of re-entries in the hospital in the emergencies.
Ernestina Menasalvas's picture
Ernestina Menasalvas
Consuelo Gonzalo's picture
Consuelo Gonzalo
Universidad Politécnica de Madrid (SP)
Juan Manuel Tunas's picture
Juan Manuel Tunas
Universidad Politécnica de Madrid
Alejandro Rodriguez Gonzalez's picture
Alejandro Rodriguez Gonzalez
Mariano Provencio's picture
Mariano Provencio
Cristina Gonzalez de Pedro's picture
Cristina Gonzalez de Pedro
Marta Mendez's picture
Marta Mendez
Olga Zaretskaia's picture
Olga Zaretskaia
Juan Luis Cruz's picture
Juan Luis Cruz
Jesus Rey's picture
Jesus Rey
Consuelo Parejo's picture
Consuelo Parejo
Reciprocity and Its Association with Treatment Adherence in an Online Breast Cancer Forum
Online health communities (OHCs) are increasingly relied upon by individuals exchanging social support for diagnoses and treatment regimens. It has been shown that social support from close relationships (e.g., family and friends) positively influence treatment adherence in offline environments, but much less is known about the online setting.
Zhijun Yin's picture
Zhijun Yin
Vanderbilt University (USA)
Lijun Song's picture
Lijun Song
Bradley Malin's picture
Bradley Malin
Combining Subgroup Discovery and Clustering to Identify Diverse Subpopulations in Cohort Study Data
Subgroup discovery (SD) exploits its full value in applications where the goal is to generate understandable models. Epidemiologists search for statistically significant relationships between risk factors and outcome in large and heterogeneous datasets encompassing information about the participants' health status gathered from questionnaires, medical examinations and image acquisition. SD algorithms can help epidemiologists by automatically detecting such relationships presented as comprehensible rules, aiming to ultimately improve prevention, diagnosis and treatment of diseases.
Uli Niemann's picture
Uli Niemann
University of Magdeburg (DE)
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
Bernhard Preim's picture
Bernhard Preim
Till Ittermann's picture
Till Ittermann
Henry VOlzke's picture
Henry VOlzke
Discovering data source stability patterns in biomedical repositories based on simplicial projections from probability distribution distances
The degree of homogeneity of statistical distributions among data sources is a critical issue when reusing data of Integrated Data Repositories (IDR). Evaluating this data source stability is of utmost importance in order to ensure a confident data reuse. This work tackles the task of discovering and classifying patterns among the statistical distributions of multiple sources in IDRs, by means of a novel approach based on simplicial projections from probability distribution distances, combined with Density-based spatial clustering of applications with noise (DBSCAN).
Pablo Ferri-Borredà's picture
Pablo Ferri-Borredà
Universitat Politècnica de València (SP)
Carlos Saez's picture
Carlos Saez
Juan Miguel Garcia-Gomez's picture
Juan Miguel Garcia-Gomez
Improving diagnosis in Obstructive Sleep Apnea with clinical data: a Bayesian network approach
In Obstructive Sleep Apnea, respiratory effort is maintained but ventilation decreases/disappears because of the partial/total occlusion in the upper airway. It affects about 4% of men and 2% of women in the world population. The aim was to define an auxiliary diagnostic method that can support the decision to perform polysomnography (standard test), based on risk and diagnostic factors. Our sample performed polysomnography between January and May of 2015.
Daniela Ferreira-Santos's picture
Daniela Ferreira-Santos
Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Disease-Based Clustering of Hospital Admission: Disease Network of Hospital Networks Approach
To improve the quality of healthcare planning, healthcare systems face challenges in identifying clusters of similar hospitals while considering varying factors. Clustering hospitals based on their admission behavior would be helpful whereas diagnosis of patients is vital in understanding variation in admission. Therefore, grouping hospitals that show similar behavior on their admission distribution while considering similarity among disease symptoms in admission is the objective of our study.
Nouf Albarakati's picture
Nouf Albarakati
Zoran Obradovic's picture
Zoran Obradovic
Temple University (USA)

GT6: eHealth studies

Carolyn Mcgregor's picture
Carolyn Mcgregor
University of Ontario (CA)
Bridget Kane's picture
Bridget Kane
Karlstad University Business School (SE)
Conference room
Session time
Friday, June 23, 2017 - 16:45 to 18:00
Comparing the Quality of Numeracy Assessment Methods in Healthcare
Numeracy skill level of patients has great influence on their preferences and priorities for the treatment options concerning their healthcare. There have been different methods for assessment of numeracy skill in healthcare domain. In our previous research we proposed a new Confidence-based Patient Numeracy Assessment (C-PNA) method. In this paper we compare it with other numeracy assessment methods in terms of newly proposed quality characteristics.
Mandana Omidbakhsh's picture
Mandana Omidbakhsh
Olga Ormandjieva's picture
Olga Ormandjieva
Concordia University
Pattern-Based Statechart Modeling Approach for Medical Best Practice Guidelines - A Case Study
Improving effectiveness and safety of patient care is an ultimate objective for medical cyber-physical systems. Many medical best practice guidelines exist in the format of hospital handbooks which are often lengthy and difficult for medical staff to remember and apply clinically. Statechart is an effective tool to model medical guidelines and enables clinical validation with medical staffs. However, some advanced statechart elements could result in high cost, such as low understandability, high difficulty in clinical validation, formal verification, and failure trace back.
Chunhui Guo's picture
Chunhui Guo
Illinois Institute of Technology (USA)
Zhicheng Fu's picture
Zhicheng Fu
Shangping Ren's picture
Shangping Ren
Yu Jiang's picture
Yu Jiang
Maryam Rahmaniheris's picture
Maryam Rahmaniheris
Lui Sha's picture
Lui Sha
Trust, Ethics and Access: Challenges in studying the work of Multidisciplinary Medical Teams
This paper highlights the challenges for researchers when undertaking research on multidisciplinary medical teams (MDTs) in real-world healthcare settings, and suggests ways in which these challenges may be addressed.
Bridget Kane's picture
Bridget Kane
Karlstad University Business School (SE)
Saturnino Luz's picture
Saturnino Luz
University of Edinburgh (UK)
Using Affective Computing to automatically adapt serious games for rehabilitation
Although many studies investigate the automatic adaptation in serious games with the goal to improve the user's motivation, the most part of Affective Computing approaches requires a high development cost and usually does not consider an intervention of health professionals in the control of adaptations that will be executed in a game. This paper describes an approach to enable affective adaptation in serious games for motor rehabilitation with the involvement of physiotherapists.
Renan Vinicius Aranha's picture
Renan Vinicius Aranha
University of Sao Paulo (BR)
Leonardo Souza Silva's picture
Leonardo Souza Silva
Marcos Lordello Chaim's picture
Marcos Lordello Chaim
Fatima De Lourdes Dos Santos Nunes's picture
Fatima De Lourdes Dos Santos Nunes

GT5 Biomedical Signal

Behnaz Ghoraani's picture
Behnaz Ghoraani
Florida Atlantic University (USA)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 14:00 to 15:30
Towards multi-parametric hub scoring of functional cortical brain networks: An electroencephalographic (EEG) study across lifespan
The attractiveness and robustness of graph theory has stimulated an unprecedented increase in studies aiming to understand the functional organization and dynamics of the brain. The investigation of brain connectomics produces a tremendous amount of data which may be examined at both a macroscopic and microscopic level. However, the interpretation of findings is still challenging. Novel methodological approaches should enhance the arsenal of the tools employed towards the understanding of the interaction of the distinct brain components.
Vasiliki Zilidou's picture
Vasiliki Zilidou
Aristotle University of Thessaloniki (GR)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Ana Vivas's picture
Ana Vivas
Maria Karagianni's picture
Maria Karagianni
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Discrimination of Preictal and Interictal Brain States from Long-Term EEG Data
Discriminating the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated for their ability to discriminate preictal from interictal EEG segments. Using public EEG data, a wide range of features is extracted from each segment and then applied to the classifiers.
Kostas M. Tsiouris's picture
Kostas M. Tsiouris
National Technical University of Athens (GR)
Vasileios C. Pezoulas's picture
Vasileios C. Pezoulas
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Michalis Zervakis's picture
Michalis Zervakis
Detection of Alcoholism based on EEG Signals and Functional Brain Network Features Extraction
Alcohol abuse disorder or alcoholism is a common disorder that leads to brain defects and associated cognitive, emotional and behavioural impairments. Finding and extracting discriminative biological markers, which are correlated to healthy brain pattern and alcoholic brain pattern, helps us to utilize automatic methods for detecting and classifying alcoholism. Many brain disorders could be detected by analyzing the Electroencephalography (EEG) signals. In this paper, for extracting the required markers we analyze the EEG signals for two groups of alcoholic and control subjects.
Negar Ahmadi's picture
Negar Ahmadi
Yulong Pei's picture
Yulong Pei
Technische Universiteit Eindhoven (NL)
Mykola Pechenizkiy's picture
Mykola Pechenizkiy
EEG Classification and Short-Term Epilepsy Prognosis using Brain Computer Interface Software
The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted and a classification algorithm is trained. The training procedure extracts knowledge in terms of a classification model, which is employed in a real-time testing.
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Konstantinos Zoulis's picture
Konstantinos Zoulis
Euripidis Glavas's picture
Euripidis Glavas
Technological Educational Institute of Epirus (GR)
Katerina D. Tzimourta's picture
Katerina D. Tzimourta
Loukas G. Astrakas's picture
Loukas G. Astrakas
Spyridon Konitsiotis's picture
Spyridon Konitsiotis
An Intermixed Color Paradigm for P300 Spellers: A Comparison with Gray-scale Spellers
P300 speller systems represent one of the most basic applications of Brain-Computer Interfaces (BCIs). A traditional P300 speller consists of a 6 by 6 grid of characters in which each column or row in this grid intensifies at random. During such intensification process, the electroencephalography (EEG) data of the subject is recorded and analyzed to determine the character to be spelled.
Mina Meshriky's picture
Mina Meshriky
Ain Shams University (EG)
Seif Eldawlatly's picture
Seif Eldawlatly
Gamal Aly's picture
Gamal Aly
Simulation of Spiral Waves and Point Sources in Atrial Fibrillation with Application to Rotor Localization
Cardiac simulations play an important role in studies involving understanding and investigating the mechanisms of cardiac arrhythmias. Today, studies of arrhythmogenesis and maintenance are largely being performed by creating simulations of a particular arrhythmia with high accuracy comparable to the results of clinical experiments. Atrial fibrillation (AF), the most common arrhythmia in the United States and many other parts of the world, is one of the major field where simulation and modeling is largely used.
Prasanth Ganesan's picture
Prasanth Ganesan
Kristina Shillieto's picture
Kristina Shillieto
Behnaz Ghoraani's picture
Behnaz Ghoraani
Florida Atlantic University (USA)

Medical Curriculum Innovations: From theory to practice

Nicolaos Dombros's picture
Nicolaos Dombros
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 11:00 to 12:30
A Pilot Medical Curriculum Analysis and Visualization According to Medbiquitous Standards
Curriculum design and implementation in higher medical education can be a great challenge. Although there are well-defined standards, such as the Curriculum Inventory and Competency Framework by MedBiquitous Consortium, existing systems are incapable of a visual representation of the various components, attributes, and relations. In this paper, we present the MEDCIN platform, a pilot tool which uses a standard-compliant curriculum data model to offer comprehensive and thorough analysis of a given curriculum.
Martin Komenda's picture
Martin Komenda
Masaryk University (CZ)
Matej Karolyi's picture
Matej Karolyi
Masaryk University (CZ)
Christos Vaitsis's picture
Christos Vaitsis
dspachos's picture
Aristotle University of Thessaloniki (GR)
Luke Woodham's picture
Luke Woodham
St George's University of London (UK)

Medical Curriculum Innovations: From theory to practice

Nicolaos Dombros's picture
Nicolaos Dombros
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 09:00 to 10:30