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Diabetic foot ulcers segmentation challenge report: Benchmark and analysis

Medical image analysis 2024年 94卷 103153页

作者： Moi Hoon Yap Bill Cassidy Michal Byra Ting-Yu Liao Huahui Yi Adrian Galdran Yung-Han Chen Raphael Brüngel Sven Koitka Christoph M Friedrich Yu-Wen Lo Ching-Hui Yang Kang Li Qicheng Lao Miguel A González Ballester Gustavo Carneiro Yi-Jen Ju Juinn-Dar Huang Joseph M Pappachan Neil D Reeves Vishnu Chandrabalan Darren Dancey Connah Kendrick Department of Computing and Mathematics Manchester Metropolitan University John Dalton Building Chester Street Manchester M1 5GD United Kingdom Lancashire Teaching Hospitals NHS Trust Preston PR2 9HT United Kingdom. Electronic address: m.yap@mmu.ac.uk. Department of Computing and Mathematics Manchester Metropolitan University John Dalton Building Chester Street Manchester M1 5GD United Kingdom. Institute of Fundamental Technological Research Polish Academy of Sciences Warsaw Poland RIKEN Center for Brain Science Wako Japan. Department of Computer Science National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu Taiwan. West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China. BCN Medtech Universitat Pompeu Fabra Barcelona Spain AIML University of Adelaide Australia. Institute of Electronics National Yang Ming Chiao Tung University No. 1001 University Road Hsinchu 300 Taiwan. Department of Computer Science University of Applied Sciences and Arts Dortmund (FH Dortmund) Emil-Figge-Str. 42 44227 Dortmund Germany Institute for Medical Informatics Biometry and Epidemiology (IMIBE) University Hospital Essen Zweigertstr. 37 45130 Essen Germany Institute for Artificial Intelligence in Medicine (IKIM) University Hospital Essen Girardetstr. 2 45131 Essen Germany. Institute for Artificial Intelligence in Medicine (IKIM) University Hospital Essen Girardetstr. 2 45131 Essen Germany Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Hufelandstr. 55 45147 Essen Germany. Institute for Medical Informatics Biometry and Epidemiology (IMIBE) University Hospital Essen Zweigertstr. 37 45130 Essen Germany. West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China. School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China BCN Medtech Universit

Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious. Recently, methods based on deep learning have shown excellent results in automatic segmentation of medical images, however, they require large-scale datasets for training, and there is limited consensus on which methods perform the best. The 2022 Diabetic Foot Ulcers segmentation challenge was held in conjunction with the 2022 International Conference on Medical Image Computing and computer Assisted Intervention, which sought to address these issues and stimulate progress in this research domain. A training set of 2000 images exhibiting diabetic foot ulcers was released with corresponding segmentation ground truth masks. Of the 72 (approved) requests from 47 countries, 26 teams used this data to develop fully automated systems to predict the true segmentation masks on a test set of 2000 images, with the corresponding ground truth segmentation masks kept private. Predictions from participating teams were scored and ranked according to their average Dice similarity coefficient of the ground truth masks and prediction masks. The winning team achieved a Dice of 0.7287 for diabetic foot ulcer segmentation. This challenge has now entered a live leaderboard stage where it serves as a challenging benchmark for diabetic foot ulcer segmentation.

关键词： Convolutional neural networks Deep learning Diabetic foot ulcers Metrics Segmentation

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Stabile Rateless Codes Design Based on Efficiency-Fairness Principles

Stabile Rateless Codes Design Based on Efficiency-Fairness P...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Yunji Li Deng Jie School of Electrical and Information Engineering Guizhou Institute of Technology Computer Basic Teaching and Research Section Officer's College of CAPF

This paper proposes an ideal model to comprehensively describe rateless codes. The model indicates that the stability is one of the important characteristics of rateless codes. Then, a novel coding scheme based on efficiency-fairness principles is proposed to improve the stability which is mainly determined by the minimum variablenode degree. The coding scheme can be splitted into two stages. The first stage mainly raises efficiency by reducing the redundant information, increasing the number of recovered messages and improving the order recovery, meanwhile taking into account the balance of variable-node degrees. The second stage focuses on fairness by raising and balancing the variablenode degrees. Simulation results show that the rateless codes from the proposed scheme have good stability. In additional, they have significant better performance than existing schemes in terms of capacity, order and uniformity recovery.

关键词： rateless codes efficiency-fairness principles balance degree distribution LT codes

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A Rateless Coding Scheme Based on Efficiency-Fairness Principles

A Rateless Coding Scheme Based on Efficiency-Fairness Princi...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Yunji Li Deng Jie School of Electrical and Information Engineering Guizhou Institute of Technology Computer Basic Teaching and Research Section Officer's College of CAPF

LT codes have excellent average performance in capacity-achievability. It is difficult to ensure the LT codes always have stable excellent performance. This paper proposes an ideal model to make a more comprehensive description for rateless codes. A novel coding scheme based on efficiencyfairness principles is proposed to improve the stability which is mainly determined by the minimum variable-node degree. The coding scheme is composed of two stages. The first stage mainly considers efficiency, which aims to reduce redundant information, increase the number of recovered packets and improve the order recovery performance as much as *** second stage focuses on fairness, which aims to balance the variable-node degree. The simulation results show that the scheme has higher minimum variable-node degree and significant improvement in performance of code rate, order and uniformity recovery compared with existing schemes.

关键词： rateless codes efficiency-fairness principles balance degree distribution LT codes

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A face recognition algorithm based on completed LBP

Boletin Tecnico/Technical Bulletin

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Boletin Tecnico/Technical Bulletin 2017年第9期55卷 624-629页

作者： He, Chun Zhang, Jing Education and Information Technology Center China West Normal University Nanchong China Basic computer teaching and research section Officer’s College of CAPF Chengdu China

As the basic LBP operator can’t extract complete feature from human face and can’t perform local features of human face wholly, a face recognition algorithm based on blocked complete local binary pattern (CLBP) is proposed. In this algorithm, we separated the CLBP operator into three parts, CLBP_C represents center pixel, CLBP_S represents signs, and CLBP_M represents values. The CLBP operator is used to extract the histogram of human face’s gray image, and then merged it into CLBP histogram;take them into comparison in similarity. In the last place, a face recognizing approach based on the nearest neighbor principle is given. Experiment shows that this algorithm has better recognition rate and is more robust than LBP based on the ORL and YALE face databases．

关键词： Local binary pattern

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research on the computer data processing modes under the big data era

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RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao 2016年第E8期2016卷 212-222页

作者： Li, Ningning Qiao, Youqing Computer Teaching and research Section School of sport communication and information techonology Shandong Sport University China Barracks Department Jinan Military General Hospital China

In this paper, we conduct research on computer data processing modes under the big data era. Big data is based on platform architecture, data and algorithms are the core of big data, but the actual application is the key to big data. Big data mining process described above, the large data mining goal must be real demand from the application, and only in connection with the specific application data and fit algorithm, the use of effective support and efficient processing platform, and dig into mode or knowledge in practice, in order to provide quantitative, rational, feasible and valuable information. From this perspective, we integrate the CUDA, Hadoop and general parallel computing methodology to construct the new framework for data processing. The effectiveness is proved and verified. © AISTI 2016.

关键词： Big data

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Community structure detection algorithm in multi-relationships complex network based on clustering

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Revista de la Facultad de Ingenieria 2016年第10期31卷 253-264页

作者： Wang, Xiaoli You, Haihui Yan, Shi Liu, Zhanbo Mu Danjiang Medical University Mu Danjiang157011 China Computer Teaching and Research Section Heilongjiang Science and Technology University Harbin150000 China

There are frequently many kinds of relationships in various real networks, which can be represented by multi-relationships complex network. It is a fundamental function for detecting the community structure for identifying the edges between structures in complex networks. Previous community structure detection algorithms often limited by the limitation of network topology structure arising from one relationship, but our algorithm based on a semi-supervised clustering algorithm and multi-subnet composited complex network can overcome the restriction. Through experiment analysis, our algorithm is compared to classical spectral clustering algorithm and nonnegative matrix factorization (NMF) by using artificial generated datasets. The Experimental results show that community structures which are divided by our proposed algorithm are more obscure. © 2016, Universidad Central de Venezuela. All rights reserved.

关键词： Clustering algorithms

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An Algorithm for Identifying Protein Complexes Based on Modified Particle Swarm Optimization

An Algorithm for Identifying Protein Complexes Based on Modi...

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2016 3rd International Conference on Management Innovation and Business Innovation(ICMIBI 2016)

作者： Yunhui Qu Computer Teaching and Research Section Xi'an Medical University

This article starts with the comparison of an algorithm for identifying various protein complexes interacting on the networks, has analyzed the shortages of the existing particle swarm optimization(PSO) and improved them so as to propose a modified PSO. From the comparison of the experimental results, it can be seen that the modified PSO mentioned in this paper can increase the identifying precision, which provides a novel idea for studying the algorithm of the protein complexes identification.

关键词： PPI networks Graph clustering algorithm Protein complexes

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The research and application of computer image processing

The research and application of computer image processing

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2016 2nd International Conference on Materials Engineering and Information Technology Applications(MEITA 2016)

作者： YunHui Qu Computer Teaching and Research Section Xi'an Medical University

ISBN: (纸本)9781510837003

research and application of image processing technology is more and more received the influence of social development,and with its own technical characteristics,in turn,affect the progress of the society as a whole *** paper simply summarized the recent development of digital image processing techniques and application status,enumerates the main advantages of the technology of digital image processing and the factors that restrict its development ideas at the same time the application and development of image processing technology in the future.

关键词： computer Image processing research Application

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