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15th International Symposium on Ambient Intelligence, ISAmI 2024

作者： Cazella, Silvio César Dihl, Fernanda Viegas Stein, Airton Tetelbom Machado, Leticia Rocha Graduate Program in Information Technologies and Health Management Federal University of Health Sciences of Porto Alegre Sarmento Leite St. 245 RS Porto Alegre90050-170 Brazil Graduate Program in Informatics in Education Federal University of Rio Grande do Sul Paulo Gama Av. 110 RS Porto Alegre90040-060 Brazil

ISBN: (纸本)9783031831164

Digital health can assist health professionals in monitoring elderly individuals after hospital discharge using technology, techniques, and practices that provide environments conducive to autonomy, comfort, and safety for already vulnerable patients. This article aims to describe the process of modeling and designing an m-Health solution that includes features for monitoring elderly patients post-discharge. The method applied was a case study, analyzing the reality of a hospital institution in Porto Alegre, Rio Grande do Sul, Brazil. The target audience for this study comprised elderly individuals with preserved cognitive functions and health professionals involved in the hospital discharge process. The modeling of the m-Health solution prototype followed the User-Centered Design method. Data collection instruments included questionnaires, interviews, and observations focusing on the two target groups. As a result, we developed a prototype, characterized by an m-Health solution with relevant functions to facilitate post-hospital discharge management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： mHealth

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Exploring Parallelism in ZynqNet AI Engine to Accelerate Image Classification in Aerospace

Exploring Parallelism in ZynqNet AI Engine to Accelerate Ima...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Eduardo Marañon Aguilar Fabio Benevenuti Fernanda Lima Kastensmidt Federal University of Rio Grande do Sul (UFRGS) Institute of Informatics Microelectronics Graduate Program (PGMICRO) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This research investigates various configurations of the Zynqnet AI Engine to accelerate image classification tasks using the SAT-6 dataset and a 4-layer CNN. The C++ code for the hardware accelerator was translated into RTL using Vivado HLS and integrated with the Zynq Processing System on a ZedBoard Zynq-7000 ARM/FPGA SoC. The AI engine was configured to work with 32 -bit floating point, 8bit fixed point and 8 -bit integer type operations, with different processing elements for parallelizing CNN tasks. Given a 64-bit data memory bus in communication with the accelerator and an 8-bit integer quantization, the variation in the number of processing elements does not result in a significant performance improvement. To address this, 8 values were grouped for reading and writing in 64-bit parallel mode, and different communication architectures were designed to improve data transfer between the accelerator and DDR memory, with optional enablement of L1D and L2 cache. The research analyzes CNN computation performance and FPGA resource usage (LUTs, FFs, BRAMs, DSPs) across configurations, providing insights into the trade-offs in FPGA-based CNN implementations, aiding the development of efficient, high-performance hardware accelerators for Deep Learning applications.

关键词： Quantization (signal) Writing Table lookup System-on-chip Convolutional neural networks Artificial intelligence Hardware acceleration Engines Field programmable gate arrays Image classification

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Impact of Balancing and Regularization on the Semantic Segmentation of Gleason Patterns

Impact of Balancing and Regularization on the Semantic Segme...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Paraíso, Eduardo Henrique S. Machado, Alexei M. C. PPGInf - Graduate Program on Informatics Pontifical Catholic University of Minas Gerais Dom José Gaspar 500 Belo Horizonte Brazil Department of Anatomy and Imaging Universidade Federal de Minas Gerais Alfredo Balena 190 Belo Horizonte Brazil

This study investigates the impact of class balancing and regularization on improving the diagnostic agreement in prostate histological images. The U-Net models applied to the Prostate Cancer Grade Assessment dataset reveal that class balancing combined with traditional loss functions contributes to an increase of up to 6 percentage points in image agreement. Combining balancing and Focal Loss can increase image classification agreement by an average of 13 percentage points compared to using an imbalanced dataset with traditional loss functions. Notably, distinguishing between Gleason patterns 3 and 4 in medical image analysis is crucial, as this distinction not only directly influences clinical decisions and the prognosis of prostate cancer patients but also emphasizes the need for careful interpretation of the data. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

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Innovative web-based microteaching model: To improve the teaching skills of prospective informatics teachers in vocational high schools

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Social Sciences and Humanities Open 2025年 11卷

作者： Sugihartini, Nyoman Elmunsyah, Hakkun Nurhadi, Didik Rahmawati, Yuni Vocational Education Doctoral Program Post Graduate School Universitas Negeri Malang Malang Indonesia Informatics Education Study Program Department of Informatics Faculty of Engineering and Vocational Universitas Pendidikan Ganesha Bali Indonesia

Prospective informatics teachers in vocational schools (VS) must master content knowledge, pedagogical skills, and technology integration to achieve learning goals. Therefore, informatics teachers in VS need to innovate to increase students' creativity. However, many prospective informatics teachers experience difficulties in this regard. This research aims to develop and implement the Web-Based Microteaching (WBM) model to improve the teaching skills of prospective informatics teachers at VS. This study uses research and development methods with the ADDIE model approach. The research results show that the WBM model effectively improves prospective informatics teachers' teaching skills, especially in planning lessons and practising basic teaching skills. This innovation is a relevant learning solution in the digital era and positively contributes to the professional development of informatics teachers. The WBM model is an innovation to provide a more intensive and structured practical experience by utilizing web technology as the main media. Through web-based learning, prospective teachers can access material, exercises and feedback independently, which is expected to accelerate pedagogical mastery. © 2025 The Authors

关键词： Microteaching model Prospective informatics teacher Teaching skills Vocational high schools Web-based

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The association between urinary tract infections and diet: a literature review

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Discover Medicine 2025年第1期2卷 1-17页

作者： Çelik, Haluk Kozan, Esra Caf, Balım Bengisu Çebi, Gizem Koç, Murat Vivosens Inc. San Francisco USA Program of Stem Cell and Tissue Engineering Institute of Graduate Education Istinye University Istanbul Türkiye Department of Health Informatics University of Health Science Hamidiye Institute of Health Sciences Istanbul Türkiye

Urinary tract infections (UTIs) are a significant global health concern, affecting millions worldwide, with particular prevalence among specific vulnerable populations. Women of sexually active age, post-menopausal women, pregnant women, individuals with diabetes, catheterized patients, childs and older indivisuals face heightened risk due to various physiological, hormonal, and anatomical factors. While anatomical abnormalities and behavioral factors contribute to UTI susceptibility, the growing challenge of antibiotic resistance has necessitated exploration of alternative treatment approaches. Non-antibiotic interventions, including probiotics, cranberry products, and natural remedies, are gaining attention for their potential role in UTI prevention and management. Recent studies suggest the efficacy of probiotics and plant extracts in promoting urinary tract health and preventing bacterial colonization. Additionally, research highlights the importance of dietary factors, such as vitamin D and probiotic intake, in reducing UTI susceptibility. This review examines the emerging shift toward non-drug strategies, offering a more comprehensive approach to UTI management while addressing the pressing concern of antibiotic resistance.

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SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for Highly Constrained Environments

SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Med...

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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024

作者： Kong, Youngbin Kim, Kwangtai Jeong, Seoi Lee, Kyu Eun Kong, Hyoun-Joong Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Seoul National University Hospital Seoul Korea Republic of Department of Surgery Seoul National University Hospital and College of Medicine Seoul Korea Republic of Department of Medicine Seoul National University College of Medicine Seoul Korea Republic of Innovative Medical Technology Research Institute Seoul National University Hospital Seoul Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University College of Medicine Seoul Korea Republic of

ISBN: (纸本)9783031818530

Medical image segmentation is a crucial step for accurate diagnosis and treatment planning, as it provides quantitative information about anatomical structures and pathological lesions in various clinical scenarios. However, the existing methodologies have limitations in terms of their generalizability and computational efficiency. In this study, we propose SwiftMedSAM, an ultra-lightweight prompt-based general model, to enable efficient medical image segmentation even in resource-constrained environments. Based on LiteMedSAM, we significantly reduced the model size and computational complexity through the hyperparameter optimization of the image encoder and mask decoder components. SwiftMedSAM showed remarkable performance across various imaging modalities enabling real-time inference in resource-limited computing environments. It achieved a validation score of 0.75 demonstrating that SwiftMedSAM outperformed the existing methodologies in terms of the trade-off between accuracy and efficiency. Owing to its unprecedented generalizability and low computational cost, SwiftMedSAM is expected to enable high-quality medical image analysis in resource-constrained settings, thereby contributing to advancements in precision medicine and telemedicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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Computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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Efficient Maximum Euclidean Distance Transform Computation in Component Trees Using the Differential Image Foresting Transform

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Journal of Mathematical Imaging and Vision 2025年第3期67卷 1-23页

作者： Silva, Dennis J. Miranda, Paulo A. V. Alves, Wonder A. L. Hashimoto, Ronaldo F. Kosinka, Jiří Roerdink, Jos B. T. M. Institute of Mathematics and Statistics University of São Paulo São Paulo Brazil Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen The Netherlands Informatics and Knowledge Management Graduate Program Nove de Julho University São Paulo Brazil

The distance transform is a crucial technique in binary image processing, assigning the distance to the nearest contour to each foreground pixel. In this extended version of our previous work, we enhance our method for computing the maximum distance transform (DT) value, now utilizing the optimized differential image foresting transform (DIFT) and improved contour extraction processes. These advancements enable more efficient computation of the maximum DT value across all connected components of a grayscale image, significantly reducing computational time by intelligently reusing DIFT trees rooted at contour points (DIFT seeds). Our optimized algorithm now achieves processing speeds that are twice as fast as our previous differential method. The proposed attribute, maximum distance, which measures the thickness of objects within the image, has proven pivotal in different image processing approaches. We showcase this through detailed illustrations of attribute opening, extinction value filters, watershed, and ultimate attribute openings.

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Athena: A collaborative approach to requirements elicitation

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COMPUTERS IN INDUSTRY 2009年第6期60卷 367-380页

作者： Laporti, Viviane Borges, Marcos R. S. Braganholo, Vanessa [a]Graduate Program in Informatics UFRJ Brazil

The quality of the requirements is crucial for a project's success. Requirements elicitation, however, is not an easy task. Viewpoint, mental model and expectation differences among users and analysts make this task hard and conflicting. In many cases, the clients are not completely sure about their real needs. In others, the current work process does not correspond to management expectations. In this paper, we try to overcome these problems by presenting Athena, an approach founded on collective knowledge to progressively build the system requirements from a narrative of user stories to the definition of use cases. Athena is a collaborative approach to elicit requirements. It is based on group storytelling, where stakeholders tell stories about current and past systems that support a given activity. The stories are merged to form a single story. Stories are then transformed into scenarios, and from scenarios to use cases. Our solution consists of a knowledge model based on stories about the system. a collective construction method, and a tool to support interactions. We have conducted experimental analyses to show the effectiveness of the proposed approach. (C) 2009 Published by Elsevier B.V.

关键词： Requirements Use cases Collaboration Group storytelling

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