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Surgical Instrument Detection for Laparoscopic Surgery using Deep Learning 19

Surgical Instrument Detection for Laparoscopic Surgery using...

19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Boonkong, Apiwat Hormdee, Daranee Sonsilphong, Suphachoke Khampitak, Kovit Khon Kaen University Embedded System RandD Group Computer Engineering Khon Kaen Thailand Khon Kaen University Data Science and Health Informatics Program Academic Affairs Khon Kaen Thailand Khon Kaen University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kaen Thailand

ISBN: (纸本)9781665485845

Laparoscopic surgery has transformed conventional open surgery. Robot-Assisted laparoscopic surgery which is minimally invasive is effective for operations in limited space. Nevertheless, the robotic system which is utilized in such surgery can inflict impair to the surgical instruments, organs, or tissues as a consequence of the narrow field of view and space available to operate, and insufficient tactile response. In laparoscopy, the surgeon observes what he operates via digital images which are displayed on a monitor. In addition to the surgeon's expertise, the image-guided surgery system could offer him significant support if that system is capable of understanding those images. Computer Vision has been able to proceed due to the emergence of Machine Learning, and especially Deep Learning. This paper applies Deep Learning for surgical instrument detection for laparoscopic surgery. The fastest model, based on results trained on the COCO 2017 Dataset on TensorFlow Framework, for each of 4 algorithms-Faster-RCNN, SSD, CenterNet and EfficientDet-from Model Zoo, along with YOLOv4 on Darknet Framework, have been investigated in this work. The evaluation metrics used are Precision, Recall and F1-score. The results reveal that each model plays its part well;however, YOLOv4 seemed to breakthrough this statement by offering grate result for all three accuracy metrics. © 2022 IEEE.

关键词： Laparoscopy

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Process Mining and Root Cause Analysis for Detecting Inefficiencies in Business Processes: an Applied Case in a Brazilian Telecommunications Provider

Process Mining and Root Cause Analysis for Detecting Ineffic...

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International Conference on Computer Supported Cooperative Work in Design

作者： Luiz Fernando Puttow Southier Esther María Rojas Krugger Cleiton Dos Santos Garcia Edson Emilio Scalabrin Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná Curitiba Brazil Centro Universitário Católica de Santa Catarina Jaragua do Sul Brazil

Process Mining (PM) is a research area that focuses on helping organizations optimize their business processes. PM provides process analysis tools and techniques with a wide range of applications described in the literature. Additionally, PM can be associated with Root Cause Analysis (RCA), a problem-solving technique based on the assumption that a problem can only be solved by addressing its underlying cause. In this study, we performed a case on a real-world Brazilian telecommunication company that provides telephony, television, and broadband Internet subscription services. We applied PM and RCA to identify the possible causes for short and long-duration services, high rework rates, and activity repetition. We split the dataset into three aspects: Support, Cancellation, and Sales related services and analyzed them separately. For RCA, we applied both decision tree classification and rule mining. We identified 53 rules over 9 decision trees that were validated by process specialists from the tech company.

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Avissa Aurellia Amadea Christy Atika Sari Eko Hari Rachmawanto Wellia Shinta Sari Mohammed S. Noori Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Based on WHO’s data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient’s diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection.

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Optimal Order of Hippocampal Place Cell Models Constructed Using Expansions of Zernike Polynomials and Power Series

Optimal Order of Hippocampal Place Cell Models Constructed U...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Sleim Mohamed I. Margham Murat Okatan Biomedical Engineering Program Istanbul Technical University Türkiye Institute of Informatics Istanbul Technical University Türkiye Department of Artificial Intelligence and Data Engineering Istanbul Technical University Türkiye

Hippocampus is a brain region that is important for the encoding and retrieval of episodic memories. The spiking activity of hippocampal place cells depends strongly on spatial location in an environment. Their position-dependent firing rate is usually modeled as a parametric function of 2-D or 3-D space. Yet, no study to date has optimized such functions using a rigorous statistical model selection procedure. Here, we model the position-dependent firing rate of hippocampal place cells using two different series expansion models and determine the optimal model type and order. Our results indicate that the optimal order is much higher than those used in earlier studies. We have observed that the models of some cells are reminiscent of the firing patterns of grid cells. These findings are important for elucidating the origins of place cell activity, for accurate assessments of the amount of position information encoded in this activity, and for the inference of position using neural decoding algorithms.

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FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning

arXiv

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arXiv 2023年

作者： Sun, Guangyu Mendieta, Matias Luo, Jun Wu, Shandong Chen, Chen Center for Research in Computer Vision University of Central Florida United States Intelligent Systems Program University of Pittsburgh United States Department of Radiology Biomedical Informatics and Bioengineering University of Pittsburgh United States

Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments. Partial model personalization has been proposed to improve the efficiency of PFL by selectively updating local model parameters instead of aggregating all of them. However, previous work on partial model personalization has mainly focused on Convolutional Neural Networks (CNNs), leaving a gap in understanding how it can be applied to other popular models such as Vision Transformers (ViTs). In this work, we investigate where and how to partially personalize a ViT model. Specifically, we empirically evaluate the sensitivity to data distribution of each type of layer. Based on the insights that the self-attention layer and the classification head are the most sensitive parts of a ViT, we propose a novel approach called FedPerfix, which leverages plugins to transfer information from the aggregated model to the local client as a personalization. Finally, we evaluate the proposed approach on CIFAR-100, OrganAMNIST, and Office-Home datasets and demonstrate its effectiveness in improving the model’s performance compared to several advanced PFL methods. Code is available at https://***/imguangyu/FedPerfix Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance 11

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Rando Setiawan, Noor Akhmad Permanasari, Adhistya Erna Rulaningtyas, Riries Suksmono, Andriyan Bayu Sitanggang, Imas Sukaesih Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Biomedical Engineering Program Dept. of Physics Universitas Airlangga Surabaya Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Ipb University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781665460309

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%. © 2022 IEEE.

关键词： Generative adversarial networks

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Analyzing Significance of Facial Features for Detection of Parkinson's Disease via Static Images

Analyzing Significance of Facial Features for Detection of P...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Chuang-Chien Chiu Shoou-Jeng Yeh Yen-Chi Sun Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Section of Neurology and Neurophysiology Cheng-Ching General Hospital Taichung Taiwan Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Parkinson's disease (PD) is a neurodegenerative disorder often characterized by motor symptoms such as hypomimia, and a reduction in facial expression. We explored the significance of facial features in identifying PD through static images, focusing on detecting hypomimia. A dataset comprised of PD patients and healthy controls' static facial images were collected and analyzed. Facial feature detection was conducted using OpenCV and Dlib libraries for the precise localization of key landmarks. In the subsequent feature extraction, parameters such as symmetry, eye aspect ratio, mouth corner distance, muscle tension, and mouth aperture ratio were used. Through the standardization of these features, meaningful comparisons across diverse images were made. By analyzing hypomimia-related features alongside traditional facial parameters, the results provide a comprehensive understanding of the role of facial expressions in PD diagnosis. The findings suggested that examining facial features in static images enhances the accuracy of PD detection and understanding of the nuanced motor symptoms associated with the disease.

关键词： Accuracy Parkinson's disease Sociology Mouth Standardization Muscles Motors

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The Banking Industry is Undergoing a Digital Transformation as Part of Industrial 4.0

The Banking Industry is Undergoing a Digital Transformation ...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Wahyu Sardjono Dewi Sagita Pranata Aditya Agung Dwi Ramdhana Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Department of Informatics Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster England Master of Management Faculty of Economics and Business University of Airlangga Surabaya Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

In the current day, digital transformation is imperative; both customer demand and market competitiveness are mostly driving that trend. The fourth industrial revolution has also affected the financial sector. One difficulty that has to be addressed is the desire from customers for quick and useful services. Traditional procedures need to be replaced with digital ones. Furthermore, banking organizations will have a competitive edge in the current market if they are successful in this digital transformation process. Successful transformation, however, does not happen by accident; in fact, the likelihood of completing this change is seen to be very low, necessitating rigorous planning for a company that is ready for the future. There are several components to the preparation in question, both internal and external. In this situation, the company's ability to combine and rearrange its resources quickly is essential for adapting to the ever-changing business environment.

关键词： Industries Leadership Companies Banking Market research Fourth Industrial Revolution Planning Faces Accidents

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An Examination of the Possible Application of Artificial Intelligence Audit Process in Organizations

An Examination of the Possible Application of Artificial Int...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Wahyu Sardjono Dewi Sagita Pranata Muhammad Isamesal Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Cyber Security Studi Program Department of Informatics Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster England Master of Management Department Faculty of Economics and Busines Universitas of Airlangga Surabaya Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This study investigates how artificial intelligence (AI) can transform audit processes, including how AI can be used in businesses to execute non-technical audit tasks and what effect this will have on those tasks. A qualitative case study methodology is used in the research design to give detailed insights into how AI is being adopted by businesses. This study aims to analyze the aspects that impact AI implementation from the viewpoints of managers, auditors, and other stakeholders. The findings show that artificial intelligence (AI) has a great deal of promise for non-technical audit procedures in businesses. AI can automate tedious jobs, evaluate large data sets to identify hazards and enhance teamwork and communication. These developments may lead to increased productivity, maybe better audit quality, and more effective processes. But there are still difficulties. Data quality is essential to the success of AI integration, and auditors may exhibit bias in favor of human professionals. Careful thought must also be given to ethical issues about transparency and possible job displacement. All things considered, this study adds significant knowledge to both the academic literature and real-world auditing applications. It improves our comprehension of the function of AI in the industry and provides useful information for people involved in information technology development, audit, and legislation.

关键词： Productivity Ethics Data analysis Reviews Data integrity Transforms Natural language processing Teamwork Stakeholders Artificial intelligence

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