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34th Medical Informatics Europe Conference, MIE 2024

作者： Krumme, Julia Wienands, Linda Teynor, Alexandra Faculty of Liberal Arts and Sciences Technical University of Applied Sciences Augsburg Germany Faculty of Computer Science Technical University of Applied Sciences Augsburg Germany Institute of Nursing Science University of Basel Switzerland

ISBN: (纸本)9781643685335

Currently, there are no adequate methods for dealing with changes in the healthcare system brought about by electronic health applications (eHealth) or the associated ethical implications in practice. This can be attributed to the lack of comprehensive interdisciplinary approaches that could support teams in integrating ethical considerations into the agile software development process. To close this gap, the DARE approach has been developed and tested in interdisciplinary collaborative research. The DARE method is a modular system designed to improve the development of ethically sound software in a deliberative, agile, and responsive manner. © 2024 The Authors.

关键词： Electronic health record

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Pose Measurement of the EndoWrist Round Tip Scissor Instrument with Optical Coherence Tomography

Pose Measurement of the EndoWrist Round Tip Scissor Instrume...

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作者： Schöne, Sandra Pol, Nirmal Kahrs, Lueder A. Medical Computer Vision and Robotics Lab University of Toronto Toronto Canada University of Stuttgart Stuttgart Germany Medical Computer Vision and Robotics Lab Institute of Biomedical Engineering University of Toronto Toronto Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga Mississauga Canada

To automate surgical (sub-)tasks in robotic surgery, the knowledge of the exact pose of the instrument is mandatory. The application of Optical Coherence Tomography (OCT) to the problem of pose measurement appears promising due to its advantages of 3D imaging and micron-scale resolution. To investigate this, 175 image sequences of the EndoWrist Round Tip Scissor Tool were acquired with an OCT system. The images differ in the opening angles of the scissor blades and the rotation angles of the entire instrument about its central axis. These image sequences were further processed through computer vision methods of the individual images followed by point cloud generation. For pose estimation, an Iterative Closest Point algorithm was implemented to register the acquired point clouds to reference point clouds created from the instrument CAD file. The implemented algorithm was able to determine the opening angle with an overall error of 2 ± 1.3 and the rotation angle with a standard deviation between several runs of 0.6 ±2.8. However, the overall processing time of (39 ± 17)s on a standard PC leaves room for further investigations. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Optical coherence tomography

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Blockchain-based Patient Recommendation System for Smart Healthcare

Blockchain-based Patient Recommendation System for Smart Hea...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Mehta, Raj Mehta, Mahek Kakkar, Riya Oza, Parita Agrawal, Smita Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat 382481 India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia

ISBN: (纸本)9798350350548

In recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc. © 2024 IEEE.

关键词： Electronic health record

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A setup for live AI support in interventional radiology

A setup for live AI support in interventional radiology

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作者： Komposch, Jan Malzacher, Till Baumgärtner, Timo Braun, Michael Roßkopf, Johannes Franz, Alfred M. Schmitz, Bernd Institute for Computer Science Ulm University of Applied Sciences Ulm Germany Neuroradiology Section District Hospital Guenzburg Guenzburg Germany Institute for Medical Engineering and Mechatronics Ulm University of Applied Sciences Ulm Germany

Artificial intelligence (AI) has the potential to support time-critical stroke treatment. In a previous study we demonstrated the feasibility of deep learning based automatic classification for thrombus detection during thrombectomies, a catheter-guided procedure to remove occlusions of cerebral vessels. However, this method has yet to be tested during a live intervention. In this work, we present a setup to integrate AI based support in an angiography suite. A classification PC was connected to the angiography by means of a real-time video connection as well as a research interface for control signals. We found that video conversion in real-time does not affect the classification result in comparison to offline classification of DICOM data. Analyzing 50 video streams of previous cases, the system could classify digital-subtraction angiography (DSA) sequences within 13.3 seconds on average. This processing time can further be reduced to an average of 7.9 seconds with GPU acceleration. Additionally, the system successfully classified two DSA sequences acquired during live thrombectomy, identifying the presence of thrombi in less than 5 seconds. So far, the classification result has only been displayed in the control room of the angiography suite to demonstrate feasibility. In the outlook, however, we also discuss how the result can be displayed directly on the angiography screen. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Radiology

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Analysis of Bio Inspired Based Hybrid Learning Model for Software Defect Prediction

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SN computer Science 2024年第7期5卷 825页

作者： Shankar, Sahana P. Chaudhari, Shilpa Shashikant Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology (Affiliated to Visvesvaraya Technological University Belgaum) Bengaluru India Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bengaluru India

The software’s quality can be ensured through software testing, which is one of the critical methods. However, it was found that testing consumes more than half of the project's total expenses. Effective and efficient software testing utilizes minimal software resources to find as many flaws in the software system as possible. This paper presents research on software defect prediction using hybrid machine learning algorithm. The algorithm incorporates the benefits of supervised as well as unsupervised learning methodology that improves the accuracy of defect prediction. The study uses publicly available datasets from the open source PROMISE repository provided by NASA MDP to perform machine learning analysis and maximize accuracy. In particular, the study suggested a hybrid learning model and conducted a comparative analysis among Naïve Bayes, Multilayer Perceptron, Random Forest, CNN, Adaboost, and KNN on four different datasets namely CM1, KC1, KC2 and PC1. With respect to F1-score, accuracy, precision, and recall, the results show that the hybrid method performed better than the other methods. The study achieved an accuracy of 93.67% (BAT + CNN) on PC1 dataset, 92% on (Bat + CNN) for CM1 and KC1 respectively. This indicates that the hybrid learning technique of (BAT + CNN) is more potent than other methodologies in prediction of defects. The research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. One useful approach for predicting software defects is the hybrid machine learning method, which can lead to improved software quality and lower maintenance costs. This aids the software testing phase by predicting the faulty modules with better accuracy thereby reducing the testing effort. The main objectives of the research include applying different machine learning algorithms and methods to the given datasets and determine the most effective ones. © The Author(s), under exclusive licence to

关键词： Bat CNN PROMISE Sparrow Search Squirrel Search Testing

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Machine learning technique for generation of human readable rules to detect software code smells in open-source software

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Life Cycle Reliability and Safety Engineering 2024年第3期13卷 277-291页

作者： Tandon, Stuti Kumar, Vijay Singh, V.B. Amity Institute of Information Technology Amity University Uttar Pradesh Department of Mathematics Amity Institute of Applied Sciences Amity University Uttar Pradesh School of Computer and Systems Sciences Jawaharlal Nehru University

Defects entering software systems due to bad programming practice during evolution and maintenance are termed code smells. Smells impacts software at design, architectural and implementation level. These flaws report depreciation in quality of software thereby wasting human effort and time. The serious outcomes of these defects motivated researchers to study code smells. This study is conducted to determine the most daunting smell of architectural, design and implementation category of code smell. We considered 60 metrics of Apache Tomcat to analyse the quality aspects of the software. Twelve Machine Learning (ML) algorithms are applied on instances of architectural smells, implementation smells and design smells extracted from source code of 15 versions of open-source software. J48 machine learning algorithm gave 98% accurate result in code smell detection. The results are validated against tenfold cross validation and the statistical parameters: Kappa statistics, Recall, Precision, etc. used for analysing the results. Human readable classification rules are generated and validated using tenfold cross validation. Results are analysed using several Precision, Recall, F-Measure, TP Rate, FP Rate, ROC Area, Kappa statistics and accuracy performance metrics. The study also focusses on architectural category which has yet not been fully explored. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.

关键词： Architectural smells Design smell Implementation smell Machine learning Software metrics Static code analysis

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Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement 20th

Bootstrap Diffusion Model Curve Estimation for High Resolut...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Huang, Jiancheng Liu, Yifan Chen, Shifeng ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819970247

Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement. However, most of them still suffer from two main problems: expensive computational cost in high resolution images and unsatisfactory performance in simultaneous enhancement and denoising. To address these problems, we propose BDCE, a bootstrap diffusion model that exploits the learning of the distribution of the curve parameters instead of the normal-light image itself. Specifically, we adopt the curve estimation method to handle the high-resolution images, where the curve parameters are estimated by our bootstrap diffusion model. In addition, a denoise module is applied in each iteration of curve adjustment to denoise the intermediate enhanced result of each iteration. We evaluate BDCE on commonly used benchmark datasets, and extensive experiments show that it achieves state-of-the-art qualitative and quantitative performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image enhancement

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Temporally consistent video colorization with deep feature propagation and self-regularization learning

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Computational Visual Media 2024年第2期10卷 375-395页

作者： Yihao Liu Hengyuan Zhao Kelvin CKChan Xintao Wang Chen Change Loy Yu Qiao Chao Dong Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China Department of Electrical&Computer Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798 Applied Research Center Tencent PCGShenzhenChina

Video colorization is a challenging and highly ill-posed *** recent years have witnessed remarkable progress in single image colorization,there is relatively less research effort on video colorization,and existing methods always suffer from severe flickering artifacts(temporal inconsistency)or unsatisfactory *** address this problem from a new perspective,by jointly considering colorization and temporal consistency in a unified ***,we propose a novel temporally consistent video colorization(TCVC)*** effectively propagates frame-level deep features in a bidirectional way to enhance the temporal consistency of ***,TCVC introduces a self-regularization learning(SRL)scheme to minimize the differences in predictions obtained using different time *** does not require any ground-truth color videos for training and can further improve temporal *** demonstrate that our method can not only provide visually pleasing colorized video,but also with clearly better temporal consistency than state-of-the-art methods.A video demo is provided at https://***/watch?v=c7dczMs-olE,while code is available at https://***/lyh-18/TCVC-Tem porally-Consistent-Video-Colorization.

关键词： video colorization temporal consistency feature propagation self-regularization

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Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data 32

Automated Bioacoustic Monitoring for South African Bird Spec...

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32nd International Conference in Central Europe on computer Graphics, Visualization and computer vision, WSCG 2024

作者： Doell, Michael Kuehn, Dominik Suessle, Vanessa Burnett, Matthew J. Downs, Colleen T. Weinmann, Andreas Hergenroether, Elke Department of Computer Science University of Applied Sciences Darmstadt Schoefferstrasse 3 Darmstadt Germany Centre for Functional Biodiversity School of Life Sciences University of KwaZulu-Natal P/Bag X01 Scottsville Pietermaritzburg3209 South Africa Algorithms for Computer Vision Imaging and Data Analysis Group University of Applied Sciences Darmstadt Schoefferstrasse 3 Darmstadt Germany

Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and challenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further models required labeled data. The developed framework automatically extracted labeled data from available platforms for selected avian species. The labeled data were embedded into recordings, including environmental sounds and noise, and were used to train convolutional recurrent neural network (CRNN) models. The models were evaluated on unprocessed real world data recorded in urban KwaZulu-Natal habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating its efficiency under noisy, real-world conditions. The proposed approach to automatically extract labeled data for chosen avian species enables an easy adaption of PAM to other species and habitats for future conservation projects. © 2024 University of West Bohemia. All rights reserved.

关键词： Biotic

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Automated Error Detection Through Specialized Task Implementation 4th

Automated Error Detection Through Specialized Task Implement...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Masanti, Corina Witschel, Hans-Friedrich Riesen, Kaspar Institute of Computer Science University of Bern Bern3012 Switzerland Institute for Informations Systems University of Applied Sciences and Arts Northwestern Switzerland Olten4600 Switzerland

ISBN: (纸本)9789819787043

The present paper introduces a multilingual data set of erroneous and correct text sentences. The novel data set marks a significant advancement from an existing corpus by incorporating additional samples and refining its overall structure. The primary purpose of this data set is to support the research and development of automated error detection systems, especially in the multilingual setting where high-quality data sets are scarce. A distinctive feature of our data set is that it incorporates only incorrect sentences and their corresponding correct versions. These sentences are sourced from a variety of texts written by native speakers from different industries, such as pharmaceuticals, banking, insurance, retail, communications, and more. Each sentence in the data set has been annotated by professional proofreaders. The paper includes a comprehensive error analysis, where we classify and scrutinize the different types of errors within the data set. By categorizing and analysing the errors in the data set, we aim to identify patterns and common issues. Additionally, we conduct a thorough experimental evaluation using a well-established language model. Our analysis assesses the classification accuracy measured over all errors and the accuracy of each specific error type. Interestingly, our results show that while some error types can be detected with an accuracy exceeding 80%, it turns out that the recognition of other error types is very difficult to solve. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Benchmark Data Set Document Analysis Error Detection

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