Fetching the paper…
Reading the bibliography…
Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data".
The rise of crowdsourcing
Howe, J. (2006) · 2006
Earlier work this paper cites.
Ground truth generation in medical imaging: a crowdsourcing-based iterative approach, In ACM Multimedia workshop on Crowdsourcing for Multimedia. Workshop on Crowdsourcing for Multimedia, ACM Multimedia
Foncubierta Rodríguez, A and Müller, H. (2012) · 2012
Earlier work this paper cites.
Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears
Luengo-Oroz, M. A, Arranz, A, and Frean, J. (2012) · 2012
Earlier work this paper cites.
Distributed medical image analysis and diagnosis through crowd-sourced games: A malaria case study
Mavandadi, S, Dimitrov, S, Feng, S, Yu, F, Sikora, U, Yaglidere, O, Padmanabhan, S, Nielsen, K, and Ozcan, A. (2012) · 2012
Earlier work this paper cites.
Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence
McKenna, M. T, Wang, S, Nguyen, T. B, Burns, J. E, Petrick, N, and Summers, R. M. (2012) · 2012
Earlier work this paper cites.
Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography
Nguyen, T. B, Wang, S, Anugu, V, Rose, N, McKenna, M, Petrick, N, Burns, J. E, and Summers, R. M. (2012) · 2012
Earlier work this paper cites.
A crowdsourcing web platform-hip joint segmentation by non-expert contributors. In Medical Measurements and Applications Proceedings (MeMeA), 2013 IEEE International Symposium on
Chávez-Aragón, A, Lee, W.-S, and Vyas, A. (2013) · 2013
Earlier work this paper cites.
Crowdsourcing as a novel technique for retinal fundus photography classification: Analysis of Images in the EPIC Norfolk Cohort on behalf of the UKBiobank Eye and Vision Consortium
Mitry, D, Peto, T, Hayat, S, Morgan, J. E, Khaw, K.-T, and Foster, P. J. (2013) · 2013
Earlier work this paper cites.
Rapid grading of fundus photos for diabetic retinopathy using crowdsourcing
Brady, C. J, Villanti, A. C, Pearson, J. L, Kirchner, T. R, Gup, O, and Shah, C. (2014) · 2014
Earlier work this paper cites.
Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers
Chandler, J, Mueller, P, and Paolacci, G. (2014) · 2014
Earlier work this paper cites.
Crowdsourcing for medical image classification
de Herrera, A. G. S, Foncubierta-Rodríguez, A, Markonis, D, Schaer, R, and Müller, H. (2014) · 2014
Earlier work this paper cites.
Preliminary results from a crowdsourcing experiment in immunohistochemistry. In Diagnostic pathology
Della Mea, V, Maddalena, E, Mizzaro, S, Machin, P, and Beltrami, C. A. (2014) · 2014
Earlier work this paper cites.
Crowd-powered experts: Helping surgeons interpret breast cancer images. In Gamification for Information Retrieval (GamifIR)
Eickhoff, C. (2014) · 2014
Earlier work this paper cites.
Mechanical Turk based system for macular OCT segmentation
Lee, A. Y and Tufail, A. (2014) · 2014
Earlier work this paper cites.
Crowdsourcing for reference correspondence generation in endoscopic images
Maier-Hein, L, Mersmann, S, Kondermann, D, and others, . (2014)b · 2014
Earlier work this paper cites.
Can Masses of Non-Experts Train Highly Accurate Image Classifiers?
Maier-Hein, L, Mersmann, S, Kondermann, D, Bodenstedt, S, Sanchez, A, Stock, C, Kenngott, H. G, Eisenmann, M, and Speidel, S. (2014)a · 2014
Earlier work this paper cites.
Crowdsourcing: harnessing the masses to advance health and medicine, a systematic review
Ranard, B. L, Ha, Y. P, Meisel, Z. F, Asch, D. A, Hill, S. S, Becker, L. B, Seymour, A. K, and Merchant, R. M. (2014) · 2014
Earlier work this paper cites.
Crowdsourcing the general public for large scale molecular pathology studies in cancer
dos Reis, F. J. C, Lynn, S, Ali, H. R, Eccles, D, Hanby, A, Provenzano, E, Caldas, C, Howat, W. J, McDuffus, L.-A, Liu, B, and others, . (2015) · 2015
Earlier work this paper cites.
How to collect segmentations for biomedical images? A benchmark evaluating the performance of experts, crowdsourced non-experts, and algorithms. In 2015 IEEE winter conference on applications of computer vision
Gurari, D, Theriault, D, Sameki, M, Isenberg, B, Pham, T. A, Purwada, A, Solski, P, Walker, M, Zhang, C, Wong, J. Y, and others, . (2015)b · 2015
Earlier work this paper cites.
How to collect segmentations for biomedical images? A benchmark evaluating the performance of experts, crowdsourced non-experts, and algorithms. In Winter Conference on Applications of Computer Vision, (WACV)
Gurari, D, Theriault, D, Sameki, M, Isenberg, B, Pham, T. A, Purwada, A, Solski, P, Walker, M, Zhang, C, Wong, J. Y, and Betke, M. (2015)a · 2015
Earlier work this paper cites.
Crowd-sourced assessment of technical skills: differentiating animate surgical skill through the wisdom of crowds
Holst, D, Kowalewski, T. M, White, L. W, Brand, T. C, Harper, J. D, Sorensen, M. D, Truong, M, Simpson, K, Tanaka, A, Smith, R, and others, . (2015) · 2015
Earlier work this paper cites.
Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd. In Pacific Symposium on Biocomputing
Irshad, H, Montaser-Kouhsari, L, Waltz, G, Bucur, O, Nowak, J, Dong, F, Knoblauch, N. W, and Beck, A. H. (2015) · 2015
Earlier work this paper cites.
Leveraging the crowd for annotation of retinal images. In International Conference of the Engineering in Medicine and Biology Society (EMBC)
Leifman, G, Swedish, T, Roesch, K, and Raskar, R. (2015) · 2015
Earlier work this paper cites.
Crowdtruth validation: a new paradigm for validating algorithms that rely on image correspondences
Maier-Hein, L, Kondermann, D, Roß, T, Mersmann, S, Heim, E, Bodenstedt, S, Kenngott, H. G, Sanchez, A, Wagner, M, Preukschas, A, and others, . (2015) · 2015
Cited alongside, same era.
A study of crowdsourced segment-level surgical skill assessment using pairwise rankings
Malpani, A, Vedula, S. S, Chen, C. C. G, and Hager, G. D. (2015) · 2015
Cited alongside, same era.
Crowdsourcing as a Screening Tool to Detect Clinical Features of Glaucomatous Optic Neuropathy from Digital Photography
Mitry, D, Peto, T, Hayat, S, Blows, P, Morgan, J, Khaw, K.-T, and Foster, P. J. (2015) · 2015
Cited alongside, same era.
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
Albarqouni, S, Baur, C, Achilles, F, Belagiannis, V, Demirci, S, and Navab, N. (2016)a · 2016
Cited alongside, same era.
Playsourcing: a novel concept for knowledge creation in biomedical research
Albarqouni, S, Matl, S, Baust, M, Navab, N, and Demirci, S. (2016)b · 2016
Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method
Irshad, H, Oh, E.-Y, Schmolze, D, Quintana, L. M, Collins, L, Tamimi, R. M, and Beck, A. H. (2017) · 2017
Later among the works it cites.
Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays
Lawson, J, Robinson-Vyas, R. J, McQuillan, J. P, Paterson, A, Christie, S, Kidza-Griffiths, M, McDuffus, L.-A, Moutasim, K. A, Shaw, E. C, Kiltie, A. E, and others, . (2017) · 2017
Later among the works it cites.
Expected exponential loss for gaze-based video and volume ground truth annotation
Lejeune, L, Christoudias, M, and Sznitman, R. (2017) · 2017
Later among the works it cites.
A survey on deep learning in medical image analysis
Litjens, G, Kooi, T, Bejnordi, B. E, Setio, A. A. A, Ciompi, F, Ghafoorian, M, van der Laak, J. A, Van Ginneken, B, and Sánchez, C. I. (2017) · 2017
Later among the works it cites.
Crowdsourcing Labels for Pathological Patterns in CT Lung Scans: Can Non-experts Contribute Expert-Quality Ground Truth?
O’Neil, A. Q, Murchison, J. T, van Beek, E. J, and Goatman, K. A. (2017) · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Early experiences with crowdsourcing airway annotations in chest CT, In Large-scale Annotation of Biomedical data and Expert Label Synthesis (MICCAI LABELS). Large-scale Annotation of Biomedical data and Expert Label Synthesis
Cheplygina, V, Perez-Rovira, A, Kuo, W, Tiddens, H, and de Bruijne, M. (2016) · 2016
Cited alongside, same era.
Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique
Greenspan, H, Van Ginneken, B, and Summers, R. M. (2016) · 2016
Cited alongside, same era.
Investigating the influence of data familiarity to improve the design of a crowdsourcing image annotation system. In Human Computation (HCOMP)
Gurari, D, Sameki, M, and Betke, M. (2016) · 2016
Cited alongside, same era.
Crowdsourcing in Computer Vision
Kovashka, A, Russakovsky, O, Fei-Fei, L, and Grauman, K. (2016) · 2016
Cited alongside, same era.
Use of Mechanical Turk as a MapReduce framework for macular OCT segmentation
Lee, A. Y, Lee, C. S, Keane, P. A, and Tufail, A. (2016) · 2016
Cited alongside, same era.
Crowd-algorithm collaboration for large-scale endoscopic image annotation with confidence. In Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Maier-Hein, L, Ross, T, Gröhl, J, Glocker, B, Bodenstedt, S, Stock, C, Heim, E, Götz, M, Wirkert, S, Kenngott, H, and others, . (2016) · 2016
Cited alongside, same era.
The accuracy and reliability of crowdsource annotations of digital retinal images
Mitry, D, Zutis, K, Dhillon, B, Peto, T, Hayat, S, Khaw, K.-T, Morgan, J. E, Moncur, W, Trucco, E, and Foster, P. J. (2016) · 2016
Cited alongside, same era.
Later among the works it cites.
Crowdsourced emphysema assessment
Ørting, S. N, Cheplygina, V, Petersen, J, Thomsen, L. H, Wille, M. M. W, and de Bruijne, M. (2017) · 2017
Later among the works it cites.
Crowdsourcing for identification of polyp-free segments in virtual colonoscopy videos. In Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
Park, J. H, Mirhosseini, S, Nadeem, S, Marino, J, Kaufman, A, Baker, K, and Barish, M. (2017) · 2017
Later among the works it cites.
Employing Weak Annotations for Medical Image Analysis Problems
Rajchl, M, Koch, L. M, Ledig, C, Passerat-Palmbach, J, Misawa, K, Mori, K, and Rueckert, D. (2017) · 2017
Later among the works it cites.
How to Create the Largest In-Vivo Endoscopic Dataset. In Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS)
Roethlingshoefer, V, Bittel, S, Kenngott, H, Wagner, M, Bodenstedt, S, Ross, T, Speidel, S, and L, M.-H. (2017) · 2017
Later among the works it cites.
Crowdsourcing for chromosome segmentation and deep classification. In Computer Vision and Pattern Recognition Workshops (CVPRW)
Sharma, M, Saha, O, Sriraman, A, Hebbalaguppe, R, Vig, L, and Karande, S. (2017) · 2017
Later among the works it cites.
Defining glioblastoma resectability through the wisdom of the crowd: a proof-of-principle study
Sonabend, A. M, Zacharia, B. E, Cloney, M. B, Sonabend, A, Showers, C, Ebiana, V, Nazarian, M, Swanson, K. R, Baldock, A, Brem, H, and others, . (2017) · 2017
Later among the works it cites.
Crowdsourcing ten years in: A review
Wazny, K. (2017) · 2017
Later among the works it cites.
A review on the applications of crowdsourcing in human pathology
Alialy, R, Tavakkol, S, Tavakkol, E, Ghorbani-Aghbologhi, A, Ghaffarieh, A, Kim, S. H, and Shahabi, C. (2018) · 2018
Later among the works it cites.
Crowdsourcing lung nodules detection and annotation. In Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
Boorboor, S, Nadeem, S, Park, J. H, Baker, K, and Kaufman, A. (2018) · 2018
Later among the works it cites.
Negative results in computer vision: A perspective
Borji, A. (2018) · 2018
Later among the works it cites.
Exploring applications of crowdsourcing to cryo-EM
Bruggemann, J, Lander, G. C, and Su, A. I. (2018) · 2018
Later among the works it cites.
Cheplygina, V, de Bruijne, M, and Pluim, J. P. (2018) · 2018
Later among the works it cites.
Crowd disagreement about medical images is informative. In Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS)
Cheplygina, V and Pluim, J. P. W. (2018) · 2018
Later among the works it cites.
Large-scale medical image annotation with quality-controlled crowdsourcing
Heim, E. (2018) · 2018
Later among the works it cites.
Combining citizen science and deep learning to amplify expertise in neuroimaging
Keshavan, A, Yeatman, J, and Rokem, A. (2018) · 2018
Later among the works it cites.
Crowd-assisted polyp annotation of virtual colonoscopy videos. In Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
Park, J. H, Nadeem, S, Marino, J, Baker, K, Barish, M, and Kaufman, A. (2018) · 2018
Later among the works it cites.
Harnessing citizen science through mobile phone technology to screen for immunohistochemical biomarkers in bladder cancer
Smittenaar, P, Walker, A. K, McGill, S, Kartsonaki, C, Robinson-Vyas, R. J, McQuillan, J. P, Christie, S, Harris, L, Lawson, J, Henderson, E, and others, . (2018) · 2018
Later among the works it cites.
Deep learning is combined with massive-scale citizen science to improve large-scale image classification
Sullivan, D. P, Winsnes, C. F, Åkesson, L, Hjelmare, M, Wiking, M, Schutten, R, Campbell, L, Leifsson, H, Rhodes, S, Nordgren, A, and others, . (2018) · 2018
Later among the works it cites.