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A large-scale transcriptional study reveals inhibition of COVID-19 related cytokine storm by traditional Chinese medicines

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science Bulletin 2021年第9期66卷 884-888,M0003页

作者： Yifei Dai Weijie Qiang Yu Gui Xue Tan Tianli Pei Kequan Lin Siwei Cai Liang Sun Guochen Ning Jianxun Wang Hongyan Guo Yimin Sun Jing Cheng Lan Xie Xun Lan Dong Wang State Key Laboratory of Southwestern Chinese Medicine Resources School of Basic Medical SciencesChengdu University of Traditional Chinese MedicineChengdu 611137China Department of Basic Medical Sciences School of MedicineTsinghua UniversityBeijing 100084China Institute of Medicinal Plant Development Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100193China School of Pharmacy Chengdu University of Traditional Chinese MedicineChengdu 611137China High Performance Computing Department National Supercomputing Center in ShenzhenShenzhen 518055China Department of Electronic and Computer Engineering College of EngineeringDrexel UniversityPhiladelphia 19104USA Dongli District Jinqiao Street Community Health Service Center Tianjin 300300China Department of Biomedical Engineering School of MedicineTsinghua UniversityBeijing 100084China School of Life Sciences Beijing University of Chinese MedicineBeijing 100029China National Engineering Research Center for Beijing Biochip Technology Beijing 102206China State Key Laboratory of Membrane Biology School of MedicineTsinghua UniversityBeijing 100084China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases Hangzhou 310003China Medical Systems Biology Research Center School of MedicineTsinghua UniversityBeijing 100084China

The application of traditional Chinese medicine(TCM)has made great contributions to the fight against the epidemic of coronavirus disease-2019(COVID-19).Despite the remarkable therapeutic effects of TCM,the molecular mechanisms of TCM formulae inhibiting COVID-19 are still not fully ***,we cφm-bined the automated high throughput sequencing-based high throughput screening(HTS^(2))assay with bioinformatics and computer-aided drug design(CADD)to investigate the molecular mechanisms of TCM-mediated therapeutic effects on COVID-19-related cytokine storm(Fig.1a).

关键词：细胞因子风暴六君子汤理中汤诊疗方案黄连解毒汤中药提取物肺炎分子机制

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Lung Cancer Classification in Histopathology Images Using Multiresolution Efficient Nets

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Computational intelligence and neuroscience 2023年第1期2023卷 7282944页

作者： Sunila Anjum Imran Ahmed Muhammad Asif Hanan Aljuaid Fahad Alturise Yazeed Yasin Ghadi Rashad Elhabob Center of Excellence in Information Technology Institute of Management Sciences Hayatabad Peshawar 25000 Pakistan. School of Computing and Information Science Anglia Ruskin University Cambridge UK. Department of Computer Science National Textile University Faisalabad Pakistan. Computer Sciences Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia. Department of Computer College of Science and Arts in Ar Rass Qassim University Ar Rass Qassim Saudi Arabia. Department of Software Engineering/Computer Science Al Ain University Al Ain UAE. College of Computer Science and Information Technology Karary University Omdurman Sudan.

Histopathological images are very effective for investigating the status of various biological structures and diagnosing diseases like cancer. In addition, digital histopathology increases diagnosis precision and provides better image quality and more detail for the pathologist with multiple viewing options and team annotations. As a result of the benefits above, faster treatment is available, increasing therapy success rates and patient recovery and survival chances. However, the present manual examination of these images is tedious and time-consuming for pathologists. Therefore, reliable automated techniques are needed to effectively classify normal and malignant cancer images. This paper applied a deep learning approach, namely, EfficientNet and its variants from B0 to B7. We used different image resolutions for each model, from 224 × 224 pixels to 600 × 600 pixels. We also applied transfer learning and parameter tuning techniques to improve the results and overcome the overfitting problem. We collected the dataset from the Lung and Colon Cancer Histopathological Image LC25000 image dataset. The dataset acquisition consists of 25,000 histopathology images of five classes (lung adenocarcinoma, lung squamous cell carcinoma, benign lung tissue, colon adenocarcinoma, and colon benign tissue). Then, we performed preprocessing on the dataset to remove the noisy images and bring them into a standard format. The model's performance was evaluated in terms of classification accuracy and loss. We have achieved good accuracy results for all variants; however, the results of EfficientNetB2 stand excellent, with an accuracy of 97% for 260 × 260 pixels resolution images.

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The Library for Hadoop Deflate Compression Based on FPGA Accelerator with Load Balance

The Library for Hadoop Deflate Compression Based on FPGA Acc...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Haixin Du Jiankui Zhang Shihao Sha Cai Ye Qiuming Luo College of Computer Science and Software Engineering Shenzhen University NHPCC/Guangdong Key Laboratory of popular HPC and Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Hadoop application will produce lots of intermediate results in the map/reduce process that requires disk I/O and network transmission. By compressing the large-scale data of intermediate result, it will greatly improve disk access efficiently and reduce program run time. Hardware-accelerated solutions have become more desirable. This paper design a multi-FPGA compression accelerator on the Hadoop platform, and the system performance analysis compared with a software-only solution that mainly uses CPU to processing. The testing programs are zpipe, TestDFSIO and Terasort. In contrast with the software-only solution. The max speedup of zpipe is 6.55X (single FPGA) and 10.24X (dual FPGA), the max speedup of TestDFSIO is 6.28X (single FPGA) and 6.28X (dual FPGA), and the max speedup of Terasort application is up to 3.25X(single FPGA) and 3.35X(dual FPGA).

关键词： Handheld computers Conferences computer science software engineering Distributed databases Indexes

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Nonreciprocal transport in a bilayer of MnBi2Te4 and Pt

arXiv

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arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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A Holistic Stream Partitioning Algorithm for Distributed Stream Processing Systems

A Holistic Stream Partitioning Algorithm for Distributed Str...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Kejian Li Gang Liu Minhua Lu Guangdong Province Key Laboratory of Popular High Performance Computers Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of biomedical engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions.

关键词： Partitioning algorithms Memory management Measurement Parallel processing Microsoft Windows Load modeling Throughput

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Implementation of the parallel mean shift-based imagesegmentation algorithm on a GPU cluster

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International Journal of Digital Earth 2019年第3期12卷 328-353页

作者： Fang Huang Yinjie Chen Li Li Ji Zhou Jian Tao Xicheng Tan Guangsong Fana School of Resources&Environment University of Electronic Science and Technology of ChinaChengduPeople’s Republic of China Institute of Remote Sensing Big Data Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China School of Construction&Management Engineering Xihua UniversityChengduPeople’s Republic of China Texas A&M Engineering Experiment Station and High Performance Research Computing Texas A&M UniversityCollege StationTXUSA International School of Software Wuhan UniversityWuhanPeople’s Republic of China

The mean shift image segmentation algorithm is very computationintensive. To address the need to deal with a large number of remotesensing (RS) image segmentations in real-world applications, this studyhas investigated the parallelization of the mean shift algorithm on asingle graphics processing unit (GPU) and a task-scheduling methodwith message passing interface (MPI)+OpenCL programming model on aGPU cluster platform. This paper presents the test results of the parallelmean shift image segmentation algorithm on Shelob, a GPU clusterplatform at Louisiana State University, with different datasets andparameters. The experimental results show that the proposed parallelalgorithm can achieve good speedups with different configurations andRS data and can provide an effective solution for RS image processingon a GPU cluster.

关键词： Mean shift algorithm GPU cluster task scheduling MPI OpenCL

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Implementing the Matrix Multiplication with DFC on Kunlun Small Scale computer

Implementing the Matrix Multiplication with DFC on Kunlun Sm...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Zheng Du Jing Zhang Shihao Sha Qiuming Luo Department of Computer Science and Technology Tsinghua University NHPCC/Guangdong Key Laboratory of popular UPC & Guangdong Province Engineering Center of China-made High Performance Data Computing System in College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512.

关键词： Distributed computing Handheld computers

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CovidCare: Transferring knowledge from existing EMR to emerging epidemic for interpretable prognosis

arXiv

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arXiv 2020年

作者： Ma, Liantao Ma, Xinyu Gao, Junyi Zhang, Chaohe Yu, Zhihao Jiao, Xianfeng Ruan, Wenjie Wang, Yasha Tang, Wen Wang, Jiangtao 1Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China 2National Engineering Research Center of Software Engineering Peking University Beijing China 3School of Electronics Engineering and Computer Science Peking University Beijing China 4School of Computing and Communications Lancaster University UK 5Division of Nephrology Peking University Third Hospital Beijing China

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from systemic life-threatening problems and need to be carefully monitored in ICUs. Thus the intelligent prognosis is in an urgent need to assist physicians to take an early intervention, prevent the adverse outcome, and optimize the medical resource allocation. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, rarity of the cases, and privacy concerns. In this paper, we propose a deep-learning-based approach, CovidCare, which leverages the existing electronic medical records to enhance the prognosis for inpatients with emerging infectious diseases. It learns to embed the COVID-19-related medical features based on massive existing EMR data via transfer learning. The transferred parameters are further trained to imitate the teacher model’s representation behavior based on knowledge distillation, which embeds the health status more comprehensively in the source dataset. We conduct the length of stay prediction experiments for patients on a real-world COVID-19 dataset. The experiment results indicate that our proposed model consistently outperforms the comparative baseline methods. CovidCare also reveals that, 1) hs-cTnI, hs-CRP and Platelet Counts are the most fatal biomarkers, whose abnormal values usually indicate emergency adverse outcome. 2) Normal values of γ-GT, AP and eGFR indicate the overall improvement of health. The medical findings extracted by CovidCare are empirically confirmed by human experts and medical literatures. Copyright © 2020, The Authors. All rights reserved.

关键词： Epidemiology

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Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

arXiv

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arXiv 2023年

作者： Jha, Debesh Sharma, Vanshali Banik, Debapriya Bhattacharya, Debayan Roy, Kaushiki Hicks, Steven A. Tomar, Nikhil Kumar Thambawita, Vajira Krenzer, Adrian Ji, Ge-Peng Poudel, Sahadev Batchkala, George Alam, Saruar Ahmed, Awadelrahman M.A. Trinh, Quoc-Huy Khan, Zeshan Nguyen, Tien-Phat Shrestha, Shruti Nathan, Sabari Gwak, Jeonghwan Jha, Ritika K. Zhang, Zheyuan Schlaefer, Alexander Bhattacharjee, Debotosh Bhuyan, M.K. Das, Pradip K. Fan, Deng-Ping Parasa, Sravanthi Ali, Sharib Riegler, Michael A. Halvorsen, Pål de Lange, Thomas Bagci, Ulas Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States SimulaMet Oslo Norway Oslo Metropolitan University Oslo Norway Jadavpur University Kolkata India Indian Institute of Technology Guwahati India Institute of Medical Technology and Intelligent Systems Technische Universität Hamburg Germany University of Oslo Norway Julius-Maximilian University of Würzburg Germany Faculty of Information Technology University of Science VNU-HCM Viet Nam Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of School of Computing University of Leeds LeedsLS2 9JT United Kingdom College of Engineering Australian National University Canberra Australia Department of Engineering Science University of Oxford Oxford United Kingdom National University of Computer and Emerging Sciences Karachi Campus Pakistan Swedish Medical Center Seattle United States Kathmandu Nepal Couger Inc Tokyo Japan Department of Software Korea National University of Transportation Chungju Korea Republic of University of Bergen Bergen Norway Department of Medicine and Emergencies Mölndal Sahlgrenska University Hospital Region Västra Götaland Sweden Department of Molecular and Clinical Medicin Sahlgrenska Academy University of Gothenburg Sweden ETH Zurich Zurich Switzerland

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a

关键词： computer aided diagnosis

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