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Communications in Computer and Information Science 2011年 205 M4D卷 468-475页

作者： Upadhyay, Vimal Shankar, Gori Amritpal Balwan, Jai Department of CS/EC St. Margaret Engineering College Neemrana Alwar Rajasthan India

ISBN: (纸本)9783642240546

This Paper introduces the concept of ad hoc network and its applications. It also presents a brief review of conventional routing protocols and various routing approaches proposed for mobile ad hoc network. A brief description of various parameters used for performance evaluation of routing algorithms is also presented. An ad hoc wireless network is "a collection of two or more devices equipped with wireless communications and network capability" that creates a network "on the fly" without any network infrastructure. They offer quick and easy network deployment in situations where it is not possible otherwise. That's why the idea of ad hoc networking is sometimes also called infrastructure less networking. Mobile Ad-hoc networks are self-organizing and self-configuring multi-hop wireless networks where, the structure of the network changes dynamically. This paper is intended to bring out the details of mainly two Position Based Routing Protocol named as LAR, DREAM. In position based routing the position (location) plays an important role in routing decisions. The position of a node can be made available by using GPS receivers or by evaluating relative positions using signal strength. Position based routing does not require knowledge of the entire network to find a route from source to destination Position based routing require that information about the physical position of the participating nodes be available and describing various parameters for LAR, DREAM used by various authors and Proposed parameters for evaluating their performance. © Springer-Verlag Berlin Heidelberg 2011.

关键词： Mobile ad hoc networks

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A 3D U-Net-Based Approach for Intracranial Aneurysm Detection 22

A 3D U-Net-Based Approach for Intracranial Aneurysm Detectio...

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Proceedings of the 2022 11th international conference on Computing and pattern recognition

作者： Tianyu Zhu Xinfeng Zhang Xiaomin Liu Xiangsheng Li Maoshen Jia Xiaoxia Chang Yuan Meng School of Information and Communication Engineering Beijing University of Technology China Medical Imaging Department Air Force Medical Center PLA China Graduate School Hebei North University China

ISBN: (纸本)9781450397056

Intracranial aneurysm refers to a neoplastic protrusion of the arterial wall caused by a localized abnormal enlargement of the cerebral artery lumen. In clinical practice, patients in the early stage of onset generally have no obvious symptoms, which is very easy to miss diagnosis. In medicine, methods such as MRA, CTA and DSA can be used to display the images of blood vessels. Among them, magnetic resonance angiography (MRA) has the advantages of low cost and small damage to the human body. Which can display the images of blood vessels in the brain. The data set used herein was based on images provided by a three-dimensional time-of-flight magnetic resonance angiography system. The main contributions of this paper are as follows: (1) We improved a classic 3D U-Net model with the combination of attention gate, residual connection, and the changes of size. Which achieved automatic segmentation of aneurysms in MRA. In the detection of aneurysms with mean diameters of 6.10mm and 7.69mm, the sensitivity was 83.4% and 86.4% respectively. (2) On the basis of this sensitivity, we achieved a low false positive rate which was 0.36 FPs/case and 0.34 FPs/case *** CONCEPTS • Computing methodologies∼Computer graphics∼image manipulation∼image processing

关键词： Intracranial aneurysm detection 3D U-Net machine learning Neural networks

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Why is the winner the best?

Why is the winner the best?

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M. D. Isensee, F. Adler, T. J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D. G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulme, A. Jannin, P. Kavur, A. E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martin-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Raedsch, T. Rafael-Patino, J. Bawa, V. Singh Speidel, S. Sudre, C. H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M. H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Bruengel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C. M. Fuller, C. Gaire, R. R. Galdran, A. Faura, A. Garcia Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martincic, T. Morais, P. Naser, M. A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. stepec, D. Subedi, R. Tochon, G. Torres, H. R. Urien, H. Vilaca, J. L. Wahid, K. A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jaeger, P. F. Kopp-Schneider, A. Maier-Hein, L. German Canc Res Ctr Div Intelligent Med Syst Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany German Canc Res Ctr Div Biostat Heidelberg Germany German Canc Res Ctr Div Med Image Comp Heidelberg Germany Univ Leeds Fac Engn & Phys Sci Sch Comp Leeds W Yorkshire England HES SO Valais Wallis Univ Appl Sci & Arts Western Inst Informat Sch Management Sierre Switzerland Lausanne Univ Hosp Dept Nucl Med & Mol Imaging Lausanne Switzerland Tech Hsch Ingolstadt Ingolstadt Germany Univ Penn Ctr Artificial Intelligence & Data Sci Integrated Philadelphia PA USA Univ Penn Ctr Biomed Image Comp & Analyt Philadelphia PA USA Univ Penn Dept Pathol & Lab Med Perelman Sch Med Philadelphia PA USA Univ Penn Dept Radiol Perelman Sch Med Philadelphia PA USA Univ Washington Dept Radiol Seattle WA USA UCL Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England UCL Dept Comp Sci London England Univ Autonoma Barcelona Barcelona Spain Comp Vis Ctr Barcelona Spain Natl Ctr Tumor Dis NCT UCC Div Translat Surg Oncol Dresden Germany Ist Italiano Tecnol Dept Adv Robot Genoa Italy Politecn Milan Dept Elect Informat & Bioengn Milan Italy IT Univ Copenhagen Copenhagen Denmark Heidelberg Univ Hosp Dept Gen Visceral & Transplantat Surg Heidelberg Germany Erasmus MC Dept Radiol & Nucl Med Biomed Imaging Grp Rotterdam Rotterdam Netherlands Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Appl Sci Western Switzerland HES SO Inst Informat Syst Sierre Switzerland Harvard Med Sch Brigham & Womens Hosp Boston MA USA Kings Coll London Sch Biomed Engn & Imaging Sci London England Univ Hosp Essen AoR Inst Artificial Intelligence Med IKIM Essen Germany Univ Nebraska Med Ctr Omaha NE USA Heidelberg Univ Hosp Dept Internal Med 3 Heidelberg Germany Rigshosp Copenhagen Univ Hosp Neurobiol Res Unit Copenhagen Denmark Arab Acad Sci & Technol Cairo Egypt CIBM Ctr Biomed Imaging L

ISBN: (纸本)9798350301298

international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. [GRAPHICS] .

关键词： cell microscopy Medical and biological vision

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Silicosis Detection Using Extended Transfer learning Model 6th

Silicosis Detection Using Extended Transfer Learning Model

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6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Sharma, Gulshan Kumar Harjule, Priyanka Agarwal, Basant Kumar, Rajesh Malaviya National Institute of Technology Jaipur Jaipur India Central University of Rajasthan Kishangarh Ajmer India

ISBN: (纸本)9783031530845

Silicosis is a prevalent and damaging occupational disease in India. The detection of silicosis on X-ray images heavily relies on the expertise of radiologists, resulting in delayed detection and diagnosis. To overcome this challenge, machine learning-based computer-aided detection methods are being explored. However, due to the lack of publicly available large databases of silicosis images, highly accurate deep-learning models for silicosis detection through X-rays are difficult to obtain. In this study, we propose a method for silicosis detection using transfer learning techniques on X-ray radiographs. We trained and tested well-known deep transfer learning models, including Vgg16, Vgg19, ResNet50, ResNet101, Densenet121, Densenet169, Densenet201, Xception, Mobilnet, InceptionV3, and Efficient B7 models. Our proposed model is based on ResNet50 in which we add additional layers, which achieved an F1 score of 0.8166. Additionally, we applied the Grad-CAM technique to produce heatmap images that highlight the important features extracted from the X-ray images This approach could serve as a feasible alternative in countries with limited access to specialized radiological equipment and trained radiologists for detecting silicosis on X-ray images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Hybrid Texture Features and Support Vector machine for Brain Tumor Cataloguing in MRI images: An Inclusive Investigation

Hybrid Texture Features and Support Vector Machine for Brain...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Neelesh Singh Josphin Selsia A Varun Ojha Shakeela Banu C Santosh Kumar P. K. Miniappan Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of MBA Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka India AIDS Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Worldwide, cancer kills more people than any other disease, and it will overtake all others as the top killer in the coming decades. The most effective ways to reduce death from brain cancer have been shown to be early identification and treatment. Recent advances in soft computing and image processing have substantially improved the understanding of medical pictures, which in turn has helped in early diagnosis. The absence of effective treatments, very high tumor heterogeneity, and low survival rates are hallmarks of GBM. Plus, there aren't a ton of MR pictures. It takes a lot of time and has a number of drawbacks to manually analyze these photographs. Maintaining a laser-like focus during the categorization process, which in turn increases the false hit rate, is also very taxing. For this reason, CAD has great promise as an automated approach for MR image analysis. Improving the accuracy of brain tumor categorization using routinely acquired T1 post-contrast MR imaging is the primary goal of this paper, which aims to develop, deploy, and assess a pattern recognition system for this purpose. Automatic brain tumor identification in MRI scans utilizing texture features extracted from the scans using a HTF and SVM is the primary focus of the first piece of research. Various assessment metrics are used to verify the experimental outcomes, including sensitivity, specificity, accuracy, matching index, overlap fraction, and extra fraction. In terms of classification accuracy, HTF with Achieving the greatest accuracy of 98% with GLCM-based vectors of features and 96%, SVM continuously showed the best performance across both conclusion tables When compared with other classifiers, HTF with SVM has a lower error in classification.

关键词： Support vector machines Location awareness Accuracy Magnetic resonance imaging Transfer learning Brain tumors Feature extraction Vectors Convolutional neural networks Medical diagnostic imaging

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1st international conference on 'Mechatronics and Artificial Intelligence' (ICMAI-2021)

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Journal of Physics: conference Series 2021年第1期1950卷

PREFACE It was a matter of pleasure to organize 1st international conference on 'Mechatronics and Artificial Intelligence' (ICMAI-2021) at Shree Guru Gobind Singh Tricentenary (SGT) University, Gurugram on 26th and 27th February 2021. The conference was organized in Association of Engineering & Technocrats, Faculty of Engineering and Technology, SGT University. The objective of this conference was to provide a common platform to scientists and researchers from all over India for exchanging their knowledge and views to deal with global challenges. The theme of this conference was not only the fundamental technology in the field of engineering science but also can change our society by exploring new horizons and continuous progress. A diverse range of topics from the neural network, digital image processing, machine learning, programmable logic controller, health care, IoT based systems, deep learning, lean manufacturing, abrasive flow machining, welding technology and simulation-based studies are featured in the conference. In addition to that, you will also hear about the incremental forming, automation integration techniques, wireless sensor techniques, non-invasive methods of diagnosis, robot assembly, data mining and various evolutionary algorithms in different applications. I hope, therefore, that you will get a chance to explore recent emerging technologies. We are delighted to deliver special thanks to IOP Publishing as our publication partner to publish the conference proceedings in JPCS. We sincerely thanks to our sponsors Council of Scientific and Industrial Research (CSIR) Human Resource Development Group (HRDG) and 'Sanranchana', SGT University Center of Research Innovation. We believe that the conference had provided the opportunity to the students and the young researchers for enriching their knowledge through interaction with eminent researchers from various NITs, IITs, state universities and reputed organizations. We hope that everybody had a great ex

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Why is the Winner the Best?

Why is the Winner the Best?

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conference on Computer Vision and pattern recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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