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A Survey of Crowdsourcing in Medical Image Analysis
Silas Nyboe Ørting,Andrew Doyle,Arno van Hilten,Matthias Hirth,Oana Inel,Christopher R. Madan,Panagiotis Mavridis,Helen Spiers,Veronika Cheplygina +8 more
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TLDR
In this article, the authors provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis, identifying common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach.Abstract:
Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.read more
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NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation.
Mohamed Amgad,Lamees A. Atteya,Hagar Hussein,Kareem Hosny Mohammed,Ehab O A Hafiz,Maha A. T. Elsebaie,Ahmed M. Alhusseiny,Mohamed Atef AlMoslemany,Abdelmagid M Elmatboly,Philip A. Pappalardo,Rokia Adel Sakr,Pooya Mobadersany,Ahmad Rachid,Anas M. Saad,Ahmad M Alkashash,Inas A. Ruhban,Anas Alrefai,Nada M. Elgazar,Ali Abdulkarim,Abo-Alela Farag,Amira Etman,Ahmed G. Elsaeed,Yahya Alagha,Yomna A. Amer,Ahmed M. Raslan,Menatalla K. Nadim,Mai A. T. Elsebaie,Ahmed Ayad,Liza E. Hanna,Ahmed Gadallah,Mohamed Elkady,Bradley Drumheller,David L. Jaye,David E. Manthey,David A. Gutman,Habiba Elfandy,Lee Cooper +36 more
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A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
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Nonnaïveté among Amazon Mechanical Turk workers: consequences and solutions for behavioral researchers
TL;DR: It is shown that crowdsourced workers are likely to participate across multiple related experiments and that researchers are overzealous in the exclusion of research participants, which can be avoided using advanced interface features that also allow prescreening and longitudinal data collection.
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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
TL;DR: In this article, a survey of semi-supervised, multiple instance and transfer learning in medical image segmentation is presented, and connections between these learning scenarios, and opportunities for future research are discussed.
Journal ArticleDOI
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
Shadi Albarqouni,Christoph Baur,Felix Achilles,Vasileios Belagiannis,Stefanie Demirci,Nassir Navab +5 more
TL;DR: An experimental study on learning from crowds that handles data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet), which gives valuable insights into the functionality of deep CNN learning from crowd annotations and proves the necessity of data aggregation integration.