Paulo H. Oliveira, L. C. Scabora, M. Cazzolato, Willian D. Oliveira, A. Traina, C. Traina
{"title":"Efficiently Indexing Multiple Repositories of Medical Image Databases","authors":"Paulo H. Oliveira, L. C. Scabora, M. Cazzolato, Willian D. Oliveira, A. Traina, C. Traina","doi":"10.1109/CBMS.2017.81","DOIUrl":"https://doi.org/10.1109/CBMS.2017.81","url":null,"abstract":"Performing content-based image retrieval over large repositories of medical images demands efficient computational techniques. The use of such techniques is intended to speed up the work of physicians, who often have to deal with information from multiple data repositories. When dealing with multiple data repositories, the common computational approach is to search each repository separately and merge the multiple results into one final response, which slows down the whole process. This can be improved if we build a mechanism able to search several repositories as if they were a single one, i.e. a mechanism to search the whole domain of medical images. Aiming at this goal, we propose the Domain Index, a new category of index structures aimed at efficiently searching domains of data, regardless of the repository to which they belong. To evaluate our proposal, we carried out experiments over multiple mammography repositories involving k Nearest Neighbor (kNN) and Range queries. The results show that images from any repository are seamlessly retrieved, even sustaining gains in performance of up to 36% in kNN queries and up to 7% in Range queries. The experimental evaluation shows that the Domain Index allows fast retrieval from multiple data repositories for medical systems, allowing a better performance in similarity queries over them.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"485 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128161426","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}
H. Sharma, N. Zerbe, Christin Boger, S. Wienert, O. Hellwich, P. Hufnagl
{"title":"A Comparative Study of Cell Nuclei Attributed Relational Graphs for Knowledge Description and Categorization in Histopathological Gastric Cancer Whole Slide Images","authors":"H. Sharma, N. Zerbe, Christin Boger, S. Wienert, O. Hellwich, P. Hufnagl","doi":"10.1109/CBMS.2017.25","DOIUrl":"https://doi.org/10.1109/CBMS.2017.25","url":null,"abstract":"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. A detailed comparative analysis of the proposed graph-based methods, also with the established low-level, object-level and high-level image descriptions is performed, that further leads to a hybrid approach combining salient visual information. Quantitative evaluation of investigated methods suggests the suitability of particular graph variants for automatic classification using H&E stained histopathological gastric cancer whole slide images based on HER2 immunohistochemistry.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"348 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134327197","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}
{"title":"Assessing the Usability of Gaze-Adapted Interface against Conventional Eye-Based Input Emulation","authors":"C. Kumar, Raphael Menges, Steffen Staab","doi":"10.1109/CBMS.2017.155","DOIUrl":"https://doi.org/10.1109/CBMS.2017.155","url":null,"abstract":"In recent years, eye tracking systems have greatly improved, beginning to play a promising role as an input medium. Eye trackers can be used for application control either by simply emulating the mouse and keyboard devices in the traditional graphical user interface, or by customized interfaces for eye gaze events. In this work, we evaluate these two approaches to assess their impact in usability. We present a gaze-adapted Twitter application interface with direct interaction of eye gaze input, and compare it to Twitter in a conventional browser interface with gaze-based mouse and keyboard emulation. We conducted an experimental study, which indicates a significantly better subjective user experience for the gaze-adapted approach. Based on the results, we argue the need of user interfaces interacting directly to eye gaze input to provide an improved user experience, more specifically in the field of accessibility.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127957195","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}
{"title":"Reciprocity and its Association with Treatment Adherence in an Online Breast Cancer Forum","authors":"Zhijun Yin, Lijun Song, B. Malin","doi":"10.1109/CBMS.2017.51","DOIUrl":"https://doi.org/10.1109/CBMS.2017.51","url":null,"abstract":"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 trusted relationships (e.g., family and friends) positively influence treatment adherence in offline environments, but much less is known about the online setting. In this study, we focus on how relationships established in an online breast cancer discussion board induce reciprocity (specifically in the form of reciprocal exchange of support) and its impact on adherence to a five-year hormonal therapy, a highly prevalent long-term treatment for breast cancers, with varying completion rates. We measure reciprocity as responses to forum posts to analyze interactions of over 6,000 patients and 100,000 responses. In doing so, we assess how reciprocity is related to time active in the OHC and the tones communicated by authors in their posts (e.g., emotions, writing styles and social tendencies). We further assess if such reciprocity is associated with treatment adherence. We find the volume of the reciprocity is positively associated with completing the five-year protocol, rather than the rate of the reciprocity or the fraction of the posts that received replies.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"326 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126543289","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}
S. Wijewickrema, Bridget Copson, Yun Zhou, Xingjun Ma, R. Briggs, J. Bailey, G. Kennedy, S. O'Leary
{"title":"Design and Evaluation of a Virtual Reality Simulation Module for Training Advanced Temporal Bone Surgery","authors":"S. Wijewickrema, Bridget Copson, Yun Zhou, Xingjun Ma, R. Briggs, J. Bailey, G. Kennedy, S. O'Leary","doi":"10.1109/CBMS.2017.10","DOIUrl":"https://doi.org/10.1109/CBMS.2017.10","url":null,"abstract":"Surgical education has traditionally relied on cadaveric dissection and supervised training in the operating theatre. However, both these forms of training have become inefficient due to issues such as scarcity of cadavers and competing priorities taking up surgeons time. Within this context, computer-based simulations such as virtual reality have gained popularity as supplemental modes of training. Virtual reality simulation offers repeated practice in a riskfree environment where standardised surgical training modules can be developed, along with systems to provide automated guidance and assessment. In this paper, we discuss the design and evaluation of such a training module, specifically aimed at training an advanced temporal bone procedure, namelycochlear implant surgery.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125098652","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}
E. Kyriacou, A. Nicolaides, A. Constantinou, M. Griffin, C. Loizou, M. Pattichis, Hamed Nasrabadi, C. Pattichis
{"title":"Carotid Bifurcation Plaque Stability Estimation Based on Motion Analysis","authors":"E. Kyriacou, A. Nicolaides, A. Constantinou, M. Griffin, C. Loizou, M. Pattichis, Hamed Nasrabadi, C. Pattichis","doi":"10.1109/CBMS.2017.52","DOIUrl":"https://doi.org/10.1109/CBMS.2017.52","url":null,"abstract":"Through this study we are presenting the initial steps towards a real time motion analysis system to predict the stability of carotid bifurcation plaques. The analysis is performed on B-mode video loops. Loops are analyzed in order to follow systole and diastole sections of the cardiac cycle and trace the motion of plaques during these periods. We had created a system that applies Farnebacks optical flow estimation method in order to estimate the flow between consecutive frames or frames at a predefined interval. Over each pair of video frames we measure velocities, orientation and magnitude of movement. The goal is to identify if a plaque has movement spread to different angles or at nearby angles. This can help us identify discordant or concordant movement. In order to verify our system we had created a set of simulated videos that have structures moving in a similar way as done in a cardiac cycle and videos that move and appear as an atherosclerotic artery. Following these tests the system has been tested and results are presented on two carotid plaques videos classified visually as having concordant and discordant plaque movement.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131470373","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}
{"title":"Recognizing Ureter and Uterine Artery in Endoscopic Images Using a Convolutional Neural Network","authors":"B. Harangi, A. Hajdu, R. Lampé, P. Torok","doi":"10.1109/CBMS.2017.137","DOIUrl":"https://doi.org/10.1109/CBMS.2017.137","url":null,"abstract":"Endoscope-based surgery has several beneficial effects regarding the rehabilitation of the patients, but has some drawbacks causing difficulties to medical experts, on the contrary. The main disadvantage is that the tactile information is loosed to expert who takes the surgical intervention. There are some organs (e.g. ureters and arteries) in the human body which have similar visual appearances, so the differentiation of them based on only visual expression via endoscopy is a challenging task to the medical experts. To support keyhole-surgery using state-of-the-art image processing solutions, we have developed a semi-automatic software which can distinguish ureters from arteries by a dedicated convolutional neural network (CNN). We have trained the CNN on 2000 images acquired during endoscopic surgery and tested on 500 test ones. 94.2% accuracy has been achieved in this two-classes classification task regarding a binary error function.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131206448","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}
Zhicheng Fu, Chunhui Guo, Shangping Ren, Yizong Ou, L. Sha
{"title":"Modeling and Integrating Human Interaction Assumptions in Medical Cyber-Physical System Design","authors":"Zhicheng Fu, Chunhui Guo, Shangping Ren, Yizong Ou, L. Sha","doi":"10.1109/CBMS.2017.50","DOIUrl":"https://doi.org/10.1109/CBMS.2017.50","url":null,"abstract":"For a cyber-physical system, its execution behaviors are often impacted by human interactive behaviors. However, the assumptions about a cyber-physical systems expected human interactive behaviors are often informally documented, or even left implicit and unspecified in system design. Unfortunately, such implicit human interaction assumptions made by safety critical cyber-physical systems, such as medical cyber-physical systems (M-CPS), can lead to catastrophes. Several recent U.S. Food and Drug Administration (FDA) medical device recalls are due to implicit human interaction assumptions. In this paper, we classify the categories of constraints in human interaction assumptions in the medical domain and develop a mathematical assumption model that allow M-CPS engineers to explicitly and precisely specify assumptions about human interactions. Algorithms are developed to integrate mathematical assumption models with system model so that the safety of the system can be not only validated by both medical and engineering professionals but also formally verified by existing formal verification tools. We use an FDA recalled medical ventilator scenario as a case study to show how the mathematical assumption model and its integration in M-CPS design may improve the safety of the ventilator and M-CPS in general.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131566448","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}
{"title":"Ecoepidemiological Simulation as a Serious Game Engine Module","authors":"M. Masoodian, S. Luz","doi":"10.1109/CBMS.2017.45","DOIUrl":"https://doi.org/10.1109/CBMS.2017.45","url":null,"abstract":"The integration of an agent-based simulation model as a component of a game engine for serious games targeting prevention and health promotion in the context of infectious diseases is described. It is argued that a combination of agent-based modelling and serious games can help provide a more realistic picture of disease spread than conventional ecoepidemiological models, by facilitating the integration of more detailed multidisciplinary expert knowledge. In addition, agent-based simulations provide engaging game mechanics, thereby fostering citizen engagement in the collection of up-to-date real-world data which can be used to improve the model.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134038353","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}
C. Bretschneider, S. Zillner, M. Hammon, P. Gass, Daniel Sonntag
{"title":"Automatic Extraction of Breast Cancer Information from Clinical Reports","authors":"C. Bretschneider, S. Zillner, M. Hammon, P. Gass, Daniel Sonntag","doi":"10.1109/CBMS.2017.138","DOIUrl":"https://doi.org/10.1109/CBMS.2017.138","url":null,"abstract":"The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features. Its underlying information extraction pipeline employs a rule-based grammar approach that is integrated with semantic technologies to determine the relevant information from the patient record. The accuracy of the system, developed with nine thousand clinical documents, reaches accuracy levels of 90% for lymph node status and 69% for the structurally most complex feature, the hormone status.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125417146","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}