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arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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Understanding Social Inequalities in Childhood Asthma: Quantifying the Mediating Role of Modifiable Early-Life Risk Factors in Seven European Birth Cohorts

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Journal of Allergy and Clinical Immunology: In Practice 2025年第6期13卷 1385-1396页

作者： Pinot de Moira, Angela Aurup, Anne V. Avraam, Demetris Zugna, Daniela Jensen, Aksel Karl Georg Welten, Marieke Cadman, Timothy de Lauzon-Guillain, Blandine Duijts, Liesbeth Elhakeem, Ahmed Esplugues, Ana Garcia-Aymerich, Judith García-Baquero, Gonzalo González Safont, Llúcia Harris, Jennifer R. Iñiguez, Carmen Jaddoe, Vincent W.V. McEachan, Rosemary R.C. Nader, Johanna L.T. Santa Marina, Loreto Swertz, Morris A. Tafflet, Muriel Vrijheid, Martine Wright, John Yang, Tiffany C. Taylor-Robinson, David Richiardi, Lorenzo Nybo Andersen, Anne-Marie School of Public Health Imperial College London London United Kingdom Department of Public Health University of Copenhagen Copenhagen Denmark Department of Nutrition Exercise and Sports University of Copenhagen Copenhagen Denmark Department of Public Health Policy and Systems University of Liverpool Liverpool United Kingdom Cancer Epidemiology Unit Department of Medical Sciences University of Turin Turin Italy Department of Pediatrics Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Generation R Study Group Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Genomics Coordination Center University Medical Center Groningen University of Groningen Groningen Netherlands Department of Genetics University Medical Center Groningen University of Groningen Groningen Netherlands Université Paris Cité and Université Sorbonne Paris Nord INSERM INRAE Centre for Research in Epidemiology and Statistics Paris France Population Health Science Bristol Medical School Bristol United Kingdom MRC Integrative Epidemiology Unit at the University of Bristol Bristol United Kingdom Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP) Madrid Spain Department of Nursing University of Valencia Valencia Spain Epidemiology and Environmental Health Joint Research Unit the Foundation for the Promotion of Health and Biomedical Research of Valencia Region Universitat Jaume I-Universitat de València Valencia Spain Barcelona Institute for Global Health (ISGlobal) Barcelona Spain Universitat Pompeu Fabra (UPF) Barcelona Spain CEADIR Faculty of Biology University of Salamanca Campus Miguel de Unamuno Avda Licenciado Méndez Nieto s/n Salamanca Spain Biodonostia Environmental Epidemiology and Child Development Group San Sebastian Spain Centre for Fertility and Health Norwegian Institute of Public Health Oslo Norway Department of Statistics and Operational Research Universitat de València València Spain Bradford Institute f

Background: Children growing up in disadvantaged socioeconomic circumstances (SECs) have an increased risk of asthma. Objective: To increase our understanding of the pathways to inequalities in asthma and potential targets for intervention by (1) examining how the social patterning of asthma and its early-life risk factors varies across countries and (2) quantifying the mediation of observed inequalities by early-life risk factors. Methods: We used data for 107,884 mother-child dyads from 7 European birth cohorts across 6 countries. Maternal education was the primary exposure measure of early-life SECs. The outcome was current asthma in childhood (3-12 years). Inequalities were examined using multivariable regression and random effects meta-analysis. The mediating effects of early-life risk factors (maternal smoking during pregnancy, adverse birth outcomes, and breastfeeding duration) were examined using counterfactual mediation analysis. Results: In meta-analysis, children of mothers with low/medium versus high education had a 17% (95% confidence interval: 8%-27%, I² = 21.6%) increased risk of asthma. Cohort-specific risk ratios ranged between 1.07 (0.97-1.18, Danish National Birth Cohort, Denmark) and 1.61 (1.08-2.40, study on the pre- & early postnatal determinants of child health & development, France). The early-life risk factors were similarly socially patterned, but with greater heterogeneity across cohorts (I² range = 66.2%-95.3%). The mediation analysis suggested that these factors play a relevant role in mediating observed inequalities (proportion mediated range: 0.08-0.72). Conclusions: There was a consistent tendency for children from disadvantaged SECs to be at greater risk of asthma in the European cohorts examined. Our results suggest that early-life risk factors partially mediate these disparities and, therefore, that public health interventions in the perinatal period may help to address inequalities in asthma. © 2025 The Autho

关键词： Birth cohort Childhood asthma Cross-cohort Inequalities Meta-analysis Socioeconomic

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Prediction of Driver Gene Matching in Lung Cancer NOG/PDX Models Based on Artificial Intelligence

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Engineering 2022年第8期8卷 102-114页

作者： Yayi He Haoyue Guo Li Diao Yu Chen Junjie Zhu Hiran C.Fernando Diego Gonzalez Rivas Hui Qi Chunlei Dai Xuzhen Tang Jun Zhu Jiawei Dai Kan He Dan Chan Yang Yang School of Medicin5 Tongji UniversityShanghai 200092China Department of Medical Oncology Shanghai Pulmonary HospitalSchool of Medicin5Tongji UniversityShanghai 200433China Department of Automation School of Electronic Information and Electrical Engineerin7Shanghai Jiao Tong UniversityShanghai 200240China Spine Center Orthopedic DepartmentShanghai Changzheng HospitalShanghai 200003China Department of Thoracic Surgery Shanghai Pulmonary HospitalSchool of Medicin5Tongji UniversityShanghai 200433China School of Materials Science and Engineerin7 Tongji UniversityShanghai 201804China Department of Thoracic Surgery Allegheny General HospitalPittsburg8PA 15212USA Department of Thoracic Surgery and Minimally Invasive Thoracic Surgery Unit(UCTMI) Coruna University HospitalCoruna 15006Spain Oncology and Immunology BU Research Service DivisionWuXi Appte3Shanghai 200131China SJTU–Yale Joint Center for Biostatistics and Data Scienc5 Department of Bioinformatics and BiostatisticsSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Division of Medical Oncology Department of Medicin5University of Colorado Anschutz Medical CampusAuror1CO 80045USA

Patient-derived tumor xenografts(PDXs)are a powerful tool for drug discovery and screening in ***,current studies have led to little understanding of genotype mismatches in PDXs,leading to massive economic ***,we established PDX models from 53 lung cancer patients with a genotype matching rate of 79.2%(42/53).Furthermore,17 clinicopathological features were examined and input in stepwise logistic regression(LR)models based on the lowest Akaike information criterion(AIC),least absolute shrinkage and selection operator(LASSO)-LR,support vector machine(SVM)recursive feature elimination(SVM-RFE),extreme gradient boosting(XGBoost),gradient boosting and categorical features(Cat Boost),and the synthetic minority oversampling technique(SMOTE).Finally,the performance of all models was evaluated by the accuracy,area under the receiver operating characteristic curve(AUC),and F1 score in 100 testing *** multivariable LR models revealed that age,number of driver gene mutations,epidermal growth factor receptor(EGFR)gene mutations,type of prior chemotherapy,prior tyrosine kinase inhibitor(TKI)therapy,and the source of the sample were powerful ***,Cat Boost(mean accuracy=0.960;mean AUC=0.939;mean F1 score=0.908)and the eight-feature SVM-RFE(mean accuracy=0.950;mean AUC=0.934;mean F1 score=0.903)showed the best performance among the ***,application of the SMOTE improved the predictive capability of most models,except Cat *** on the SMOTE,the ensemble classifier of single models achieved the highest accuracy(mean=0.975),AUC(mean=0.949),and F1 score(mean=0.938).In conclusion,we established an optimal predictive model to screen lung cancer patients for non-obese diabetic(NOD)/Shi-scid,interleukin-2 receptor(IL-2R)γ^(null)(NOG)/PDX models and offer a general approach for building predictive models.

关键词： Machine learning Patient-derived tumor xenografts NOG mice

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Challenges and Experiences Extending the cBioPortal for Cancer Genomics to a Molecular Tumor Board Platform

Challenges and Experiences Extending the cBioPortal for Canc...

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2021 European Federation for Medical Informatics (EFMI) Special Topic Conference, STC 2021

作者： Reimer, Niklas Unberath, Philipp Busch, Hauke Börries, Melanie Metzger, Patrick Ustjanzew, Arsenij Renner, Christopher Prokosch, Hans-Ulrich Christoph, Jan Group for Medical Systems Biology Lübeck Institute of Experimental Dermatology Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Department of Medical Informatics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute of Medical Bioinformatics and Systems Medicine Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Heidelberg Germany Johannes Gutenberg-University School of Medicine Mainz Germany Medical Data Science Martin-Luther-University Halle-Wittenberg Faculty of Medicine Halle Germany

ISBN: (纸本)9781643682365

In Molecular Tumor Boards (MTBs), therapy recommendations for cancer patients are discussed. To aid decision-making based on the patient's molecular profile, the research platform cBioPortal was extended based on users' requirements. Additionally, a comprehensive dockerized workflow was developed to support the deployment of cBioPortal and connected services. In this work, we present the challenges and experiences of nearly two years of implementing and deploying an MTB platform based on cBioPortal and compare those to findings of a previous study. © 2021 The European Federation for Medical Informatics (EFMI) and IOS Press. All rights reserved.

关键词： Genome

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AutoScore-ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes

arXiv

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arXiv 2022年

作者： Saffari, Seyed Ehsan Ning, Yilin Feng, Xie Chakraborty, Bibhas Volovici, Victor Vaughan, Roger Ong, Marcus Eng Hock Liu, Nan Centre For Quantitative Medicine Duke-NUS Medical School Singapore Singapore Programme In Health Services And Systems Research Duke-NUS Medical School Singapore Singapore Department Of Biostatistics And Bioinformatics Duke University DurhamNC United States Department Of Statistics And Data Science National University Of Singapore Singapore Singapore Department Of Neurosurgery Erasmus MC Stroke Center Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Department Of Public Health Erasmus University Rotterdam Netherlands Department Of Emergency Medicine Singapore General Hospital Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute Of Data Science National University Of Singapore Singapore Singapore

Background Risk prediction models are useful tools in clinical decision-making which help with risk stratification and resource allocations and may lead to a better health care for patients. AutoScore is a machine learning-based automatic clinical score generator for binary outcomes. This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes. Methods The AutoScore-Ordinal framework is generated using the same 6 modules of the original AutoScore algorithm including variable ranking, variable transformation, score derivation (from proportional odds models), model selection, score fine-tuning, and model evaluation. To illustrate the AutoScore-Ordinal performance, the method was conducted on electronic health records data from the emergency department at Singapore General Hospital over 2008 to 2017. The model was trained on 70% of the data, validated on 10% and tested on the remaining 20%. Results This study included 445,989 inpatient cases, where the distribution of the ordinal outcome was 80.7% alive without 30-day readmission, 12.5% alive with 30-day readmission, and 6.8% died inpatient or by day 30 post discharge. Two point-based risk prediction models were developed using two sets of 8 predictor variables identified by the flexible variable selection procedure. The two models indicated reasonably good performance measured by mean area under the receiver operating characteristic curve (0.785 and 0.793) and generalized c-index (0.737 and 0.760), which were comparable to alternative models. Conclusion AutoScore-Ordinal provides an automated and easy-to-use framework for development and validation of risk prediction models for ordinal outcomes, which can systematically identify potential predictors from high-dimensional data. © 2022, CC BY.

关键词： Records management

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Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

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Science China(Life Sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason Tieliu Shi Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of Biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research Center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea Faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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OP04 Endoscopic healing in paediatric IBD perpetuates a persistent signature characterized by pathogenic Th17 cells and tissue-associated molecular and microbial drivers of disease

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Journal of Crohn's and Colitis 2024年第SUPPLEMENT_1期18卷 i6-i6页

作者： Siebert, K Köhler, N Faro, T Matchado, M Hölz, H Jarosch, S Koletzko, S Ohnmacht, C List, M Busch, D Haller, D Schwerd, T LMU Klinikum Dr. von Hauner Children’s Hospital- Department of Paediatrics Munich Germany Technical University of Munich LipiTUM- Chair of Experimental Bioinformatics Freising Germany Technical University of Munich Big Data in Biomedicine- Chair of Experimental Bioinformatics Freising Germany Technical University of Munich- Institute for Medical Microbiology Immunology and Hygiene Munich Germany Technical University of Munich and Helmholtz Center Munich Center of Allergy and Environment ZAUM- Research Center for Environmental Health Neuherberg Germany Technical University of Munich Chair of Nutrition and Immunology- ZIEL-Institute for Food and Health Freising Germany

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia Computer Science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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Pyramid Scene Parsing Net Model for Automated Paddy Field Map using SPOT 6 Satellite Images

Pyramid Scene Parsing Net Model for Automated Paddy Field Ma...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)

作者： Yaya Heryadi Edy Irwansyah Eka Miranda Haryono Soeparno Herlawati Kiyota Hashimoto Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Faculty of Technology and Environment Prince of Songkla University Phuket Thailand

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops.

关键词： Training Productivity Deep learning Satellites Semantic segmentation Crops Predictive models

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