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2021 European Federation for Medical informatics (EFMI) Special Topic Conference, STC 2021

作者： Kilintzis, Vassilis Alexandropoulos, Vasileios C. Beredimas, Nikolaos Maglaveras, Nicos Laboratory of Computing Medical Informatics and Biomedical Imaging Technologies Medical School Aristotle University of Thessaloniki Greece

ISBN: (纸本)9781643682365

The process of maintenance of an underlying semantic model that supports data management and addresses the interoperability challenges in the domain of telemedicine and integrated care is not a trivial task when performed manually. We present a methodology that leverages the provided serializations of the Health Level Seven International (HL7) Fast Health Interoperability Resources (FHIR) specification to generate a fully functional OWL ontology along with the semantic provisions for maintaining functionality upon future changes of the standard. The developed software makes a complete conversion of the HL7 FHIR Resources along with their properties and their semantics and restrictions. It covers all FHIR data types (primitive and complex) along with all defined resource types. It can operate to build an ontology from scratch or to update an existing ontology, providing the semantics that are needed, to preserve information described using previous versions of the standard. All the results based on the latest version of HL7 FHIR as a Web Ontology Language (OWL-DL) ontology are publicly available for reuse and extension. © 2021 The European Federation for Medical informatics (EFMI) and IOS Press. All rights reserved.

关键词： Interoperability

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Beyond Time and Accuracy: Strategies in Visual Problem-Solving 25

Beyond Time and Accuracy: Strategies in Visual Problem-Solvi...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Mörth, Eric Kostic, Zona Gehlenborg, Nils Pfister, Hanspeter Beyer, Johanna Nobre, Carolina Department of Biomedical Informatics Harvard Medical School Boston MA United States SEAS Harvard University Boston MA United States Visual Computing Group Harvard University Cambridge MA United States University of Toronto Toronto ON Canada

ISBN: (纸本)9798400713941

In this paper, we explore viewers' strategies in visual problem-solving tasks. We build on the traditional metrics of accuracy and time to better understand the learning that occurs as individuals interact with visualizations. We conducted an in-lab eye-tracking user study with 53 participants from diverse demographic backgrounds. Using questions from the Visualization Literacy Assessment Test (VLAT), we examined participants' problem-solving strategies. We employed a mixed-methods approach capturing quantitative data on performance and gaze patterns, as well as qualitative data through think-alouds and sketches by participants as they reported on their problem-solving approach. Our analysis reveals not only the various cognitive strategies leading to correct answers but also the nature of mistakes and the conceptual misunderstandings that underlie them. This research contributes to the enhancement of visualization design guidelines by incorporating insights into the diverse strategies and cognitive processes employed by users. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： problem-solving strategies Visualization visualization literacy

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On the Effectiveness of Virtual IMU Data for Eating Detection with Wrist Sensors

On the Effectiveness of Virtual IMU Data for Eating Detectio...

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Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous computing and the 2022 ACM International Symposium on Wearable Computers

作者： Yash Jain Hyeokhyen Kwon Thomas Ploetz School of Interactive Computing Georgia Institute of Technology United States Department of Biomedical Informatics Emory University United States

ISBN: (纸本)9781450394239

The successful training of human activity recognition (HAR) systems typically heavily depends on the availability of sufficient amounts of labeled sensor data. Unfortunately, obtaining large-scale labeled datasets is usually expensive and often limited by practical and / or privacy reasons. Recently, IMUTube was introduced to tackle this data scarcity problem through generating weakly-labeled, virtual IMU data from unconstrained video repositories, such as YouTube. IMUTube was demonstrated to be very effective at classifying locomotion or gym exercises that involve large movements of body parts. Yet, many important daily activities, such as eating, do not exhibit such substantial body (part) movements but are rather based on more subtle, fine-grained motions. This work explores the utility of IMUTube for such subtle motion activities with specific, exemplary application to eating detection. We found that–surprisingly–IMUTube is also very effective for this challenging HAR domain. Our experiment demonstrates that eating recognition systems benefit from virtual IMU data extracted from video datasets with significant improvements of recognition accuracy (increases of 8.4% and 5.9% F1-score absolute for both curated and in-the-wild video datasets, respectively relative to 71.5% F1-score of the baseline), which is encouraging for the broader use of systems like IMUTube.

关键词： Virtual IMU data Daily Activity Recognition Eating recognition

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Explainable Hybrid Model for Presaging Tumor Stage Classification with Survival Survey

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SN Computer Science 2025年第4期6卷 1-24页

作者： Manna, Sweta Mistry, Sujoy Zhao, Zhongming Dahal, Keshav Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Nadia W.B Kolkata 741249 India Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX United States School of Engineering and Computing University of the West of Scotland Paisley PA1 2BE United Kingdom

Tumor stage classification with a survival survey during detection time is the most crucial part of cancer treatment. The survivability period is directly associated with the early detection of the stage. This study developed a hybrid model to accurately classify the tumor (T) stages with the survivability analysis. The study aims to provide an explainable Artificial Intelligence framework (such as SHAP, and SHAPASH), through which the trustability and interpretability of the proposed model can increase. Along with the XAI, this hybrid approach uses two statistical models, ANOVA and LASSO with the standard TNM (tumor, node, metastasis) method for the T-stage classification. Afterward, the survivability analysis for each T stage is shown by the Kaplan–Meier (KM) method, proportion hazard ratio (Cox). Where KM is used to represent the survivability of individual tumor stages in months and the Cox regression shows the risk factors of the events. Initially, the proposed model was examined on a full dataset that contained malignant and benign data for evaluating the performance of the model, therefore stage classification for T1, T2, and T3 performs for malignant data. The K-fold and repeated-stratified K-fold cross-validation are used to measure the accuracy of the whole dataset for lung and breast cancer. For lung cancer, maximum accuracy was attained by MLP–97.64 in K-fold cross-validation. In breast cancer maximum accuracy achieved by GPC-99.57 in repeated-stratified K-fold cross validation. The results of both datasets are compared with the state-of-the-art models. The study contributed by representing the significance of individual features for tumor staging using the advanced approach of XAI, the SHAPASH interface. The accuracy of the T1, T2, and T3 stages based on malignant data was demonstrated with various classifier models with cross-validation. Finally, it is expected that executing that approach will improve patient outcomes. By using, advanced artificial in

关键词： ANOVA Breast cancer Deep learning Explainable artificial intelligence F-score Gaussian process classification LASSO Lung cancer Machine learning Random-Forest Shapely additive explanation XGBoost

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Breast Cancer Detection on X-Tray Mammogram Images 3

Breast Cancer Detection on X-Tray Mammogram Images

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Azmi, Muhammad Arrif Amirul Mustafa, Wan Azani Alquran, Hiam Aziz, Azwa Abdul Mansor, Muhammad Naufal Alzubaidi, Laith H. Hussein, A.H.A. Universiti Malaysia Perlis Faculty of Electrical Engineering Technology UniCITI Alam Campus Sungai Chuchuh Padang Besar Perlis Malaysia Yarmouk University Department of Biomedical Systems and Informatics Engineering Irbid Jordan Faculty of Informatics & Computing Terengganu Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Breast cancer (BC) is a common cancer affecting women everywhere in world. Mammography is identified and efficient technique to detect primary BC. The aim of project is for detect BC on mammogram in order to categorize disease by image processing when comparing with a previous technique. Through utilizing conventional methods, it creates it complex for radiology to detect cancer from patient's breast. In addition, there are environmental disturbances and technical problems if using the old method. Image processing techniques was separated to numerous elements. The elements are input, pre-processing, segmentation, morphological, object classification and classification. First pre-processing was done through Weiner and Median filter. Then, thresholding method on segmentation and finally, morphological will eliminate limitations at a segmentation. The image classified into 2 classes like normal and tumor. Both type of images analyzed based on elements. Additionally, it comprises a building of Graphical User Interface (GUI) which is utilized to generate the system as user-friendly. The developed model attain accuracy of 93.71 %, specificity of 82.53 % sensitivity of 94.36% for tumorous images. © 2023 IEEE.

关键词： Breast Cancer Classification Graphical User Interface Image Processing MATLAB

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Beyond Entertainment: An Investigation of Externalization Design in Video Games

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Computer Graphics Forum 2025年

作者： Becker, F. Warnking, R.P. Brückler, H. Blascheck, T. University of Stuttgart Institute for Visualization and Interactive Systems Germany Heidelberg University Medical Faculty Mannheim Department of Biomedical Informatics Germany University of Osnabrück Institute for Computer Science Visual Computing Group Germany

This article investigates when and how video games enable players to create externalizations in a diverse sample of 388 video games. We follow a grounded-theory approach, extracting externalizations from video games to explore design ideas and relate them to practices in visualization. Video games often engage players in problem-solving activities, like solving a murder mystery or optimizing a strategy, requiring players to interpret heterogeneous data—much like tasks in the visualization domain. In many cases, externalizations can help reduce a user's mental load by making tangible what otherwise only lives in their head, acting as external storage or a visual playground. Over five coding phases, we created a hierarchy of 277 tags to describe the video games in our collection, from which we extracted 169 externalizations. We characterize these externalizations along nine dimensions like mental load, visual encodings, and motivations, resulting in 13 categories divided into four clusters: quick access, storage, sensemaking, and communication. We formulate considerations to guide future work, looking at tasks and challenges, naming potentials for inspiration, and discussing which topics could advance the state of externalization. © 2025 The Author(s). Computer Graphics Forum published by Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd.

关键词： Flow visualization

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Diagnosis of Alzheimer's disease and Mild Cognitive Impairment using Rethinking and Deep Neural Networks 24

Diagnosis of Alzheimer's disease and Mild Cognitive Impairme...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Kostopoulos, Evangelos Diamantaras, Konstantinos Chouvarda, Ioanna Tsolaki, Magda Department of Information and Electronic Engineering International Hellenic University Sindos57400 Greece Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki54124 Greece Greek Association of Alzheimer's Disease and Related Disorders Thessaloniki54643 Greece

ISBN: (纸本)9798350339598

The early diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), are important for doctors to treat patients as soon as possible. In this paper we focus exclusively on the use of EEG for the Machine Learning based diagnosis of MCI and AD. The choice of EEG is made because this modality is relatively inexpensive, non-invasive and easily available even outside the clinical environment. Unlike the majority of ML algorithms for AD diagnosis which treat EEG as an 1-D signal we propose a 2-D approach. We first split the EEG channel signals into fixed length segments and then we reshape them into 2-D images used to train a powerful VGG-16 deep Convolutional Neural Network model. In order to improve the performance of the VGG-16 model and avoid overfitting, we employed a powerful pruning method called *** study two experimental tasks: (a) classifying segments into AD/MCI or Health Control (HC) categories (b) classifying subjects (patients and healthy individuals) into the same categories. Our results showed that the proposed method can be used as a tool to diagnose AD at early stages with accuracy over 98.8. © 2023 IEEE.

关键词： Diagnosis

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Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

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Computers, Materials & Continua 2022年第8期72卷 2175-2190页

作者： Mustafa Musa Jaber Salman Yussof Amer S.Elameer Leong Yeng Weng Sura Khalil Abd Anand Nayyar Institute of Informatics and Computing in Energy Universiti Tenaga NasionalMalaysia Department of Computer Science Dijlah University CollageBaghdad10021Iraq Biomedical Informatics College University of Information Technology and CommunicationsBaghdadIraq Faculty of Information Technology Duy Tan UniversityDa Nang550000Viet Nam Graduate School Duy Tan UniversityDa Nang550000Viet Nam Department of Computer Science Al-turath University CollegeBaghdadIraq

Artificial intelligence plays an essential role in the medical and health *** convolution networks offer valuable services and help create automated systems to perform medical image ***,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected ***,an optimized deep convolution network is utilized for analyzing disease-affected regions in this *** disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning *** method identifies affected pixels by the gray wolf hunting *** convolution network uses an automatic learning function that predicts the disease affected by previous imaging *** optimized algorithm-based selected regions are further examined using the distribution pattern-matching *** pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation *** process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

关键词： Artificial intelligence medical field gray wolf-optimized deep convolution networks distribution pattern-matching rule

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Evaluating 3D Printed VR Controller Prototypes to Increase VR Accessibility for Older Adults 3

Evaluating 3D Printed VR Controller Prototypes to Increase V...

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3rd IEEE International Conference on Human-Machine Systems, ICHMS 2022

作者： Gluck, Aaron Solini, Hannah Maiti, Kuntal Brinkley, Julian Clemson University School of Computing ClemsonSC United States Clemson University Human Factors Psychology ClemsonSC United States Clemson University Biomedical Data Science & Informatics ClemsonSC United States

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Virtual reality (VR) is transforming how technology benefits society, including the areas of training, socialization, and entertainment. While VR's growth has been substantial, VR hardware and applications are inaccessible in many ways, excluding a large population from experiencing its benefits. Current VR controllers potentially create an accessibility barrier for older adults who may experience confusion with the complexity of buttons, triggers, and joysticks. In the presented work, we recruited ten older adult participants to develop and evaluate 3D printed VR controller prototypes for usability and learnability. A series of participatory design focus groups discussed participants' needs, opinions, and design preferences, while usability scales evaluated the prototypes. Iterations were implemented between sessions based on this feedback to further refine the designs. Findings suggest that a simplified VR controller layout can decrease confusion and increase learnability, potentially improving older adults' ability to interact with virtual environments. © 2022 IEEE.

关键词： 3D printers

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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Computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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