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An efficient and effective automatic image annotation using positive and negative example images

ICIC Express Letters 2011年第8 B期5卷 2927-2932页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Bao, Cuizhu Dong, Yuying Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Avenue Changchun 130012 China Institute of Computer Graphics and Image Processing Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China College of Computer Science and Engineering Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China College of Life Science Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China

Automatic image annotation is an active topic and difficult task in computer vision domain, which has attracted more and more researchers' attention. Many approaches have been proposed to automatically annotate images. To improve the annotation quality, most of them have profound principlessophisticated modelsand high time complexities. How to efficiently annotate image is becoming one key issue in the field of computer vision. We propose a very efficient and effective approach called keyword vector classification approach, whose model is very simple. Experiments on Corel5K dataset show that the annotation performance of the our proposed approach is almost four times better than translation model and twice better than CMRM (in terms of precision, recall and F1) while the time cost of ours surprisingly decreases. In addition, to our knowledge, our proposed approach outperforms most of existing ones in terms of N+. © 2011 ISSN 1881-803X.

关键词： image retrieval

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Efficient MedSAMs: Segment Anything in Medical images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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A Temporally and Spatially Explicit, Mechanistic Estimation of Ragweed Pollen Concentrations Across Europe

SSRN

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SSRN 2023年

作者： Makra, László Matyasovszky, István Tusnády, Gábor Ziska, Lewis H. Hess, Jeremy J. Nyúl, László G. Chapman, Daniel S. Coviello, Luca Gobbi, Andrea Jurman, Giuseppe Furlanello, Cesare Brunato, Mauro Damialis, Athanasios Charalampopoulos, Athanasios Müller-Schärer, Heinz Schneider, Norbert Szabó, Bence Sümeghy, Zoltán Páldy, Anna Magyar, Donát Bergmann, Karl-Christian Deák, Áron József Mikó, Edit Thibaudon, Michel Oliver, Gilles Albertini, Roberto Bonini, Maira Šikoparija, Branko Radišić, Predrag Mitrović Josipović, Mirjana Gehrig, Regula Severova, Elena Rodinkova, Victoria Palamarchuk, Olena Stjepanović, Barbara Ianovici, Nicoleta Berger, Uwe Kofol Seliger, Andreja Rybníček, Ondřej Myszkowska, Dorota Dąbrowska-Zapart, Katarzyna Majkowska-Wojciechowska, Barbara Weryszko-Chmielewska, Elzbieta Grewling, Lukasz Rapiejko, Piotr Malkiewicz, Malgorzata Šaulienė, Ingrida Shalaboda, Valentina Prykhodo, Olexander Maleeva, Anna Ščevková, Jana Bullock, James M. Institute of Economics and Rural Development Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Department of Meteorology Eötvös Loránd University P.O.B. 32 Budapest1518 Hungary Alfréd Rényi Institute of Mathematics P.O.B 127 Budapest1364 Hungary Mailman School of Public Health Columbia University New YorkNY10032 United States Department of Global Health University of Washington SeattleWA98105 United States Department of Image Processing and Computer Graphics University of Szeged P.O.B. 652 Szeged6701 Hungary Biological and Environmental Sciences Faculty of Natural Sciences University of Stirling Scotland StirlingFK9 4LA United Kingdom University of Trento Enogis s.r.l. Trento Italy Bruno Kessler Foundation Trento Italy HK3 Lab Orobix Life Srl Rovereto Bergamo Italy Department of Information Engineering and Computer Science University of Trento Trento Italy Terrestrial Ecology and Climate Change Department of Ecology School of Biology Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece Departement of Biology Unit of Ecology and Evolution University of Fribourg FribourgCH-1700 Switzerland National Institute of Environmental Health Albert Flórián út 2-6 Budapest1097 Hungary Institute of Allergology Charité Hindenburgdamm 30 Berlin12203 Germany Institute of Animal Science and Wildlife Management Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Réseau National de Surveillance Aérobiologique 11 chemin de la Creuzille Le Plat du Pin Brussieu696905 France Laboratory of Hygiene and Aerobiology Department of Medicine and Surgery University of Parma U.O. Medicina Interna di Continuità Azienda Ospedaliero-Universitaria di Parma Via Gramsci 14 Parma43126 Italy Hygiene and Public Health Service via Spagliardi 19 Parabiago Milan20015 Italy BioSensе Institute Research Institute for Information Technologies in Biosystems University of Novi Sad Dr

Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant’s reproductive and growth (phenological, pollen production and frost-related) characteristics. DL model performances were consistently better for estimating annual total pollen concentrations than those of the GM approach. These are the first published modeled maps using altitude correction and phenology to recover missing pollen information. We created a web page (http://***/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions. © 2023, The Authors. All rights reserved.

关键词： Climate change

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