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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Qiong Chang Aolong Zha Weimin Wang Xin Liu Masaki Onishi Tsutomu Maruyama Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

Mobile stereo matching systems are becoming more important in many applications such as auto-driving and autonomous robots. However, to maintain its low power consumption, mobile platforms have only limited hardware resources. Accurate stereo matching methods require a high computational complexity, and it is difficult to maintain both acceptable accuracy and processing speed on the mobile platforms. To solve this trade-off, in this paper, we propose a novel acceleration approach for a well-known matching algorithm Zero-means Normalized Cross Correlation (ZNCC), and show its effectiveness on a Jetson TX2 embedded GPU. By combining our new approach, Z 2 - ZNCC, with the Semi-Global Matching (SGM) algorithm, our system achieves a low error rate of 7.76% while keeping 28 fps for 1242×375 pixels images with the maximum disparity of 128 on the KITTI 2015 dataset. This performance is higher than previous state-of-the-art system on the same hardware platform.

关键词： Correlation Power demand Graphics processing units Hardware System-on-chip Acceleration Field programmable gate arrays

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OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

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Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

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Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

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Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： artificial intelligence Heavy metals Sensitivity analysis Soil contamination

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DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NL...

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IEEE International Symposium on Innovations in Intelligent systems and applications (INISTA)

作者： Kareem Amin Stelios Kapetanakis Nikolaos Polatidis Klaus-Dieter Althoff Andreas Dengel Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

With widespread modernization, digitization and transformations of most of industries, artificial intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.

关键词： Machine learning Training Neural networks Natural language processing Cognition Buildings Task analysis

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Explainable AI unlocks temperature-driven oscillatory viscoelastic transitions in sesame protein isolate during integrated heating–cooling cycles

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Food Hydrocolloids 2025年 169卷

作者： Mustafa Tahsin Yilmaz Abdulaziz S. Alkabaa Furkan Turker Saricaoglu Ahmad H. Milyani Osman Gul Mahmut Ekrem Parlak Wael S. Hassanein Department of Industrial Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science (AIADS) King Abdulaziz University Jeddah Saudi Arabia Department of Food Engineering Faculty of Engineering and Natural Science Bursa Technical University Bursa Türkiye Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Excellence in Intelligent Engineering Systems (CEIES) King Abdulaziz University Jeddah Saudi Arabia Department of Food Engineering Faculty of Engineering and Architecture Kastamonu University Kastamonu Türkiye

The temperature-dependent viscoelastic behavior of sesame protein isolate (SePI) gels was investigated across integrated heating–cooling cycles (25–95 °C) under oscillatory rheometry (10 % strain, 0.1 Hz). Experiments were performed across a range of treatment conditions, including pressure levels of 0, 50, and 100 MPa and ionic concentrations (IC) of 0–200 mM. Empirical results showed that storage modulus ( G ′) consistently exceeded loss modulus ( G ″), particularly during cooling, indicating elastic-dominant gelation. application of pressure and ionic concentration (IC) treatments enhanced viscoelastic recovery, yet condition-specific nonlinear trends in G ′ and G ″ responses—particularly across temperature cycles—and associated hysteresis effects remained difficult to isolate from aggregated empirical trends alone. To address these limitations, stacking ensemble mimicry models were developed and explainable AI (XAI) methods, including SHAP values, partial dependence plots (PDPs), and variance-based sensitivity indices (VBSIs), were employed. The XGBMeta-Stacker and LGBMMeta-Stacker models predicted G ′ and G ″ with high accuracy, achieving R 2 values above 0.94 for both training and testing sets. Despite variability and outliers in the temperature sweep dataset, both ensemble models showed strong predictive alignment with actual values, highlighting the robustness of the stacking strategy in complex rheological modeling. XAI analyses uncovered temperature-driven oscillatory viscoelastic transitions—repeated patterns unlikely to be captured when heating and cooling cycles are examined separately, particularly between 25 and 75°C—highlighting the necessity of integrated cycle analysis to reveal such behavior and enabling quantitative ranking of temperature, pressure, and IC influences across the domain. Temperature emerged as the dominant driver of G ′ and G ″ transitions, while pressure exerted stronger effects on viscous behavior under high-intensity conditions.

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Glass transition of amorphous polymeric materials informed by machine learning

APL Machine Learning

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APL Machine Learning 2023年第2期1卷

作者： Anwen Hu Yongdi Huang Qionghai Chen Wanhui Huang Xiaohui Wu Lihong Cui Yining Dong Jun Liu College of Mathematics and Physics Beijing University of Chemical Technology Beijing 100029 People’s Republic of China State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical Technology Beijing 100029 People’s Republic of China Interdisciplinary Research Center for Artificial Intelligence Beijing University of Chemical Technology Beijing 100029 People’s Republic of China School of Data Science and Hong Kong Institute for Data Science Centre for Systems Informatics Engineering City University of Hong Kong Tat Chee Avenue Kowloon 999077 Hong Kong

The glass transition temperature (Tg) is used to determine thermophysical properties of polymer materials and is often considered one of the most important descriptors. Methods for predicting various physical properties of materials based on machine learning algorithms and key molecular descriptors are efficient and accurate. However, it still needs improvements because an overly complex model is less practical and difficult to generalize. In addition, obtaining a large number of samples to achieve accurate predictions remains a challenge due to the complex and lengthy experimental process. In this work, based on Tg of 100 polymers, we use a feature selection algorithm combining FeatureWiz and the least absolute shrinkage and selection operator to quickly select molecular descriptors that are minimally redundant and maximally relevant to Tg. The processed dataset is interpolated from the original dataset using the nearest neighbor interpolation algorithm to solve the data deficiency problem. Finally, the synthetic minority oversampling technique algorithm is used to solve the data imbalance problem. The augmented dataset is used to construct the extreme gradient boosting prediction model to achieve good prediction accuracy. The experimental results demonstrate the robustness of the proposed model and the accuracy of its prediction results.

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SECP-AKE: Secure and efficient certificateless-password-based authenticated key exchange protocol for smart healthcare systems

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Journal of systems Architecture 2025年 167卷

作者： Zhiqiang Zhao Xuexian Hu Yining Liu Jianghong Wei Yuanjun Xia Yangfan Liang State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou 450000 Henan China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou 325027 Zhejiang China School of Computer and Information Security Guilin University of Electronic Technology Guilin 541004 Guangxi China College of Information Science and Engineering the Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems the Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province and the Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing 314001 Zhejiang China

Due to the importance and sensitivity of medical data, the security protection and privacy preservation of the Healthcare Internet of Things (IoT) are current research hotspots. However, existing research schemes still suffer from incomplete security properties, imperfect authentication mechanisms, and inadequate privacy preservation. Therefore, this paper presents SECP-AKE, a secure and efficient certificateless-password-based authenticated key exchange protocol for IoT-based smart healthcare, which enables batch authentication, resists physical attacks, and provides strong anonymity. Specifically, using certificateless cryptography, the SECP-AKE protocol enables batch authentication of authorized users and devices while also resolving the key escrow problem. In particular, the SECP-AKE protocol incorporates Physical Unclonable Functions (PUFs) to resist physical attacks, thus enhancing device security and ensuring reliable medical service delivery. Additionally, the design of a pseudonym update mechanism can achieve user unlinkability, thereby providing enhanced privacy preservation. The results from both formal verification using SVO logic and informal security analyses demonstrate that the SECP-AKE protocol is secure and offers more comprehensive security properties. Meanwhile, the use of a well-known automated security verification tool Scyther further evaluates the protocol’s security reliability. Ultimately, comparative experiments on communication overhead and computational overhead demonstrate that the SECP-AKE protocol is efficient and feasible compared to state-of-the-art existing works.

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Extracting critical clinical indicators and survival prediction of lung cancer from pathology reports using large language models

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Computers in Biology and Medicine 2025年 195卷

作者： Yung-Chun Chang Shih-Hsin Hsiao Wen-Chao Yeh Yan-Chun Hsing Chu-Chun Wang Cheng-Yu Chen Graduate Institute of Data Science Taipei Medical University New Taipei City Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei City Taiwan Division of Pulmonary Medicine Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Division of Pulmonary Medicine Department of Internal Medicine Taipei Medical University Hospital Taipei City Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Department of Medical Imaging Taipei Medical University Taipei City Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City Taiwan Translational Imaging Research Center Taipei Medical University Taipei City Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan

Lung cancer remains the leading cause of cancer deaths in many developed countries, primarily due to late-stage diagnosis. Histopathology, the gold standard for diagnosis, often results in semi-structured pathological reports containing complex information that can hinder timely clinical decision-making. This study evaluates the effectiveness of pre-trained language models (PLMs) in extracting 16 critical features from pathology reports (as defined by National Comprehensive Cancer Network (NCCN) guidelines) and then using them for survival prediction in patients with advanced-stage (AJCC stage IIIB–IV) lung cancer. Approximately 20,000 pathology reports from 4600 lung cancer patients across three Taipei Medical University (TMU) hospitals were analyzed. Rigorous validation included 10-fold cross-validation on 3047 annotated reports from TMU Hospital and external validation with 1258 reports from National Taiwan University Hospital (NTUH). Among the models tested, the fine-tuned LLaMA 3 demonstrated superior accuracy, achieving a 92 % F 1 -score in feature extraction and a 70 % F 1 -score in survival prediction. Cross-hospital validation confirmed its robustness and generalizability, highlighting its potential for clinical application. This research underscores the transformative potential of PLMs in lung cancer care to enable automated extraction of critical clinical indicators and improve survival prediction, which can enhance decision-making efficiency and patient outcomes.

关键词： Clinical feature extraction Lung cancer pathology Natural language processing Pre-trained language models Survival prediction

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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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