Published on in Vol 14, No 6 (2012): Nov-Dec

Crowdsourcing Malaria Parasite Quantification: An Online Game for Analyzing Images of Infected Thick Blood Smears

Crowdsourcing Malaria Parasite Quantification: An Online Game for Analyzing Images of Infected Thick Blood Smears

Crowdsourcing Malaria Parasite Quantification: An Online Game for Analyzing Images of Infected Thick Blood Smears

Journals

  1. Corpas M. Crowdsourcing the Corpasome. Source Code for Biology and Medicine 2013;8(1) View
  2. Ponti M, Hillman T, Kullenberg C, Kasperowski D. Getting it Right or Being Top Rank: Games in Citizen Science. Citizen Science: Theory and Practice 2018;3(1):1 View
  3. Kahn M, Maurer R, Wartman S, Sachs B. A Case for Change. Academic Medicine 2014;89(9):1216 View
  4. Good B, Loguercio S, Griffith O, Nanis M, Wu C, Su A. The Cure: Design and Evaluation of a Crowdsourcing Game for Gene Selection for Breast Cancer Survival Prediction. JMIR Serious Games 2014;2(2):e7 View
  5. Hill S, Merchant R, Ungar L. Lessons Learned About Public Health from Online Crowd Surveillance. Big Data 2013;1(3):160 View
  6. Yu B, Willis M, Sun P, Wang J. Crowdsourcing Participatory Evaluation of Medical Pictograms Using Amazon Mechanical Turk. Journal of Medical Internet Research 2013;15(6):e108 View
  7. Ortiz-Ruiz A, Postigo M, Gil-Casanova S, Cuadrado D, Bautista J, Rubio J, Luengo-Oroz M, Linares M. Plasmodium species differentiation by non-expert on-line volunteers for remote malaria field diagnosis. Malaria Journal 2018;17(1) View
  8. Dasgupta N, Freifeld C, Brownstein J, Menone C, Surratt H, Poppish L, Green J, Lavonas E, Dart R. Crowdsourcing Black Market Prices For Prescription Opioids. Journal of Medical Internet Research 2013;15(8):e178 View
  9. Nguyen V, Benchoufi M, Young B, Ghosn L, Ravaud P, Boutron I. A scoping review provided a framework for new ways of doing research through mobilizing collective intelligence. Journal of Clinical Epidemiology 2019;110:1 View
  10. Saunders D, Bex P, Woods R. Crowdsourcing a Normative Natural Language Dataset: A Comparison of Amazon Mechanical Turk and In-Lab Data Collection. Journal of Medical Internet Research 2013;15(5):e100 View
  11. Veeman M, Reeves W. Quantitative and in toto imaging in ascidians: Working toward an image‐centric systems biology of chordate morphogenesis. genesis 2015;53(1):143 View
  12. Liu F, Qin Y, Meng S, Zhang W, Tang W, Han L, Liu C, Zhang Y, Huang S, Zheng H, Yang B, Tucker J. HIV self-testing among men who have sex with men in China: a qualitative implementation research study. Journal of Virus Eradication 2019;5(4):220 View
  13. Wang C, Han L, Stein G, Day S, Bien-Gund C, Mathews A, Ong J, Zhao P, Wei S, Walker J, Chou R, Lee A, Chen A, Bayus B, Tucker J. Crowdsourcing in health and medical research: a systematic review. Infectious Diseases of Poverty 2020;9(1) View
  14. Chu K, Smith Z, Wachsmann-Hogiu S. Development of inexpensive blood imaging systems: where are we now?. Expert Review of Medical Devices 2015;12(5):613 View
  15. Créquit P, Mansouri G, Benchoufi M, Vivot A, Ravaud P. Mapping of Crowdsourcing in Health: Systematic Review. Journal of Medical Internet Research 2018;20(5):e187 View
  16. DAS D, MUKHERJEE R, CHAKRABORTY C. Computational microscopic imaging for malaria parasite detection: a systematic review. Journal of Microscopy 2015;260(1):1 View
  17. Wazny K. Applications of crowdsourcing in health: an overview. Journal of Global Health 2018;8(1) View
  18. Rhoads D, Mathison B, Bishop H, da Silva A, Pantanowitz L. Review of Telemicrobiology. Archives of Pathology & Laboratory Medicine 2016;140(4):362 View
  19. Della Mea V, Maddalena E, Mizzaro S, Machin P, Beltrami C. Preliminary results from a crowdsourcing experiment in immunohistochemistry. Diagnostic Pathology 2014;9(S1) View
  20. Bourquard A, Pablo-Trinidad A, Butterworth I, Sánchez-Ferro Á, Cerrato C, Humala K, Fabra Urdiola M, Del Rio C, Valles B, Tucker-Schwartz J, Lee E, Vakoc B, Padera T, Ledesma-Carbayo M, Chen Y, Hochberg E, Gray M, Castro-González C. Non-invasive detection of severe neutropenia in chemotherapy patients by optical imaging of nailfold microcirculation. Scientific Reports 2018;8(1) View
  21. Linares M, Postigo M, Cuadrado D, Ortiz-Ruiz A, Gil-Casanova S, Vladimirov A, García-Villena J, Nuñez-Escobedo J, Martínez-López J, Rubio J, Ledesma-Carbayo M, Santos A, Bassat Q, Luengo-Oroz M. Collaborative intelligence and gamification for on-line malaria species differentiation. Malaria Journal 2019;18(1) View
  22. Mitsakakis K, Hin S, Müller P, Wipf N, Thomsen E, Coleman M, Zengerle R, Vontas J, Mavridis K. Converging Human and Malaria Vector Diagnostics with Data Management towards an Integrated Holistic One Health Approach. International Journal of Environmental Research and Public Health 2018;15(2):259 View
  23. Irshad H, Oh E, Schmolze D, Quintana L, Collins L, Tamimi R, Beck A. Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method. Scientific Reports 2017;7(1) View
  24. Jorda J, Sawaya M, Yeates T. CrowdPhase: crowdsourcing the phase problem. Acta Crystallographica Section D Biological Crystallography 2014;70(6):1538 View
  25. Good B, Su A. Crowdsourcing for bioinformatics. Bioinformatics 2013;29(16):1925 View
  26. Ilakkuvan V, Tacelosky M, Ivey K, Pearson J, Cantrell J, Vallone D, Abrams D, Kirchner T. Cameras for Public Health Surveillance: A Methods Protocol for Crowdsourced Annotation of Point-of-Sale Photographs. JMIR Research Protocols 2014;3(2):e22 View
  27. Sescleifer A, Francoisse C, Lin A. Systematic Review: Online Crowdsourcing to Assess Perceptual Speech Outcomes. Journal of Surgical Research 2018;232:351 View
  28. Wang X, Mudie L, Baskaran M, Cheng C, Alward W, Friedman D, Brady C. Crowdsourcing to Evaluate Fundus Photographs for the Presence of Glaucoma. Journal of Glaucoma 2017;26(6):505 View
  29. Ranard B, Ha Y, Meisel Z, Asch D, Hill S, Becker L, Seymour A, Merchant R. Crowdsourcing—Harnessing the Masses to Advance Health and Medicine, a Systematic Review. Journal of General Internal Medicine 2014;29(1):187 View
  30. Wang X, Mudie L, Brady C. Crowdsourcing. Current Opinion in Ophthalmology 2016;27(3):256 View
  31. Michelucci P, Dickinson J. The power of crowds. Science 2016;351(6268):32 View
  32. Polin M, Siddiqui N, Comstock B, Hesham H, Brown C, Lendvay T, Martino M. Crowdsourcing: a valid alternative to expert evaluation of robotic surgery skills. American Journal of Obstetrics and Gynecology 2016;215(5):644.e1 View
  33. Vera J, Santos G. Can Gamification Contribute to Computer Modeling-Driven Biomedical Research?. Frontiers in Physiology 2018;9 View
  34. Das R, Keep B, Washington P, Riedel-Kruse I. Scientific Discovery Games for Biomedical Research. Annual Review of Biomedical Data Science 2019;2(1):253 View
  35. Fernández D, Harel D, Ipeirotis P, McAllister T. Statistical considerations for crowdsourced perceptual ratings of human speech productions. Journal of Applied Statistics 2019;46(8):1364 View
  36. Weiner M. The Potential of Crowdsourcing to Improve Patient-Centered Care. The Patient - Patient-Centered Outcomes Research 2014;7(2):123 View
  37. Meyer A, Longhurst C, Singh H. Crowdsourcing Diagnosis for Patients With Undiagnosed Illnesses: An Evaluation of CrowdMed. Journal of Medical Internet Research 2016;18(1):e12 View
  38. G. Rodrigo E, Aledo J, Gámez J. Machine learning from crowds: A systematic review of its applications. WIREs Data Mining and Knowledge Discovery 2019;9(2) View
  39. Poostchi M, Silamut K, Maude R, Jaeger S, Thoma G. Image analysis and machine learning for detecting malaria. Translational Research 2018;194:36 View
  40. Wilman H, Ebejer J, Shi J, Deane C, Knapp B. Crowdsourcing Yields a New Standard for Kinks in Protein Helices. Journal of Chemical Information and Modeling 2014;54(9):2585 View
  41. Brady C, Villanti A, Pearson J, Kirchner T, Gupta O, Shah C. Rapid Grading of Fundus Photographs for Diabetic Retinopathy Using Crowdsourcing. Journal of Medical Internet Research 2014;16(10):e233 View
  42. Cui L, Carter R, Zhang G. Evaluation of a Novel Conjunctive Exploratory Navigation Interface for Consumer Health Information: A Crowdsourced Comparative Study. Journal of Medical Internet Research 2014;16(2):e45 View
  43. Brady C, Mudie L, Wang X, Guallar E, Friedman D. Improving Consensus Scoring of Crowdsourced Data Using the Rasch Model: Development and Refinement of a Diagnostic Instrument. Journal of Medical Internet Research 2017;19(6):e222 View
  44. Bassi H, Lee C, Misener L, Johnson A. Exploring the characteristics of crowdsourcing: An online observational study. Journal of Information Science 2020;46(3):291 View
  45. Zhai H, Lingren T, Deleger L, Li Q, Kaiser M, Stoutenborough L, Solti I. Web 2.0-Based Crowdsourcing for High-Quality Gold Standard Development in Clinical Natural Language Processing. Journal of Medical Internet Research 2013;15(4):e73 View
  46. Wasik S. Modeling Biological Systems Using Crowdsourcing. Foundations of Computing and Decision Sciences 2018;43(3):219 View
  47. Han T, Sun H, Song Y, Fang Y, Liu X. Find truth in the hands of the few: acquiring specific knowledge with crowdsourcing. Frontiers of Computer Science 2021;15(4) View
  48. McCullough M, Auslander A, Nagengast E, Yao C, Swanson J, Magee W. The Use of Crowdsourcing Technology to Evaluate Preoperative Severity in Patients With Unilateral Cleft Lip in a Multiethnic Population. Journal of Craniofacial Surgery 2021;32(2):482 View
  49. Tsueng G, Nanis M, Fouquier J, Mayers M, Good B, Su A, Wren J. Applying citizen science to gene, drug and disease relationship extraction from biomedical abstracts. Bioinformatics 2020;36(4):1226 View
  50. Delgado L, Postigo M, Cuadrado D, Gil-Casanova S, Martínez Á, Linares M, Merino P, Gimo M, Blanco S, Bassat Q, Santos A, García-Basteiro A, Ledesma-Carbayo M, Luengo-Oroz M, Quinn F. Remote analysis of sputum smears for mycobacterium tuberculosis quantification using digital crowdsourcing. PLOS ONE 2022;17(5):e0268494 View
  51. Brown M, Grossenbacher M, Martin‐Raugh M, Kochert J, Prewett M. Can you crowdsource expertise? Comparing expert and crowd‐based scoring keys for three situational judgment tests. International Journal of Selection and Assessment 2021;29(3-4):467 View
  52. Maturana C, de Oliveira A, Nadal S, Bilalli B, Serrat F, Soley M, Igual E, Bosch M, Lluch A, Abelló A, López-Codina D, Suñé T, Clols E, Joseph-Munné J. Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review. Frontiers in Microbiology 2022;13 View
  53. Alialy R, Tavakkol S, Tavakkol E, Ghorbani-Aghbologhi A, Ghaffarieh A, Kim S, Shahabi C. A Review on the Applications of Crowdsourcing in Human Pathology. Journal of Pathology Informatics 2018;9(1):2 View
  54. Elor A, Whittaker S, Kurniawan S, Michael S. BioLumin: An Immersive Mixed Reality Experience for Interactive Microscopic Visualization and Biomedical Research Annotation. ACM Transactions on Computing for Healthcare 2022;3(4):1 View
  55. Sadeghianasl S, Hofstede A, Wynn M, Turkay S, Myers T. Process Activity Ontology Learning From Event Logs Through Gamification. IEEE Access 2021;9:165865 View
  56. Rädsch T, Reinke A, Weru V, Tizabi M, Schreck N, Kavur A, Pekdemir B, Roß T, Kopp-Schneider A, Maier-Hein L. Labelling instructions matter in biomedical image analysis. Nature Machine Intelligence 2023;5(3):273 View
  57. Bonney K. Neglected Tropical Diseases: A Case for Promoting Innovation and Transdisciplinary Perspectives in Liberal Arts Education. Journal of College Science Teaching 2020;49(5):23 View
  58. Rubio J, Moyà-Alcover G, Jaume-i-Capó A, Petrović N. Crowdsourced human-based computational approach for tagging peripheral blood smear sample images from Sickle Cell Disease patients using non-expert users. Scientific Reports 2024;14(1) View
  59. Washington P. A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health. Journal of Medical Internet Research 2024;26:e51138 View

Books/Policy Documents

  1. Huang M, Hamarneh G. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. View
  2. Laato S, Hyrynsalmi S, Paloheimo M. Software Business. View
  3. Albarqouni S, Matl S, Baust M, Navab N, Demirci S. Deep Learning and Data Labeling for Medical Applications. View
  4. Cacciola T, Martino M. Comprehensive Healthcare Simulation: Surgery and Surgical Subspecialties. View
  5. O’Neil A, Murchison J, van Beek E, Goatman K. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. View
  6. Santos C, van de Haterd J, Hutchinson K, Khan V, Markopoulos P. End-User Development. View
  7. Pereira Santos C, Khan V, Markopoulos P. Social Informatics. View
  8. Kundu T, Anguraj D, Shetty N. Trends in Sustainable Computing and Machine Intelligence. View