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To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort.
How experience and training influence mammography expertise
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The nature of expertise in radiology
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Interactive graph cuts for optimal boundary & region segmentation of objects in nd images, in: Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, IEEE. pp. 105–112
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Grabcut: Interactive foreground extraction using iterated graph cuts, in: ACM transactions on graphics (TOG), ACM. pp. 309–314
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Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (simple)
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Labelme: a database and web-based tool for image annotation
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T., 2008 · 2008
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Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy
Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D., 2009 · 2009
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Fast free-form deformation using graphics processing units
Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S., 2010 · 2010
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A study on continuous max-flow and min-cut approaches, in: Computer Vision and Pattern Recognition–CVPR 2010, IEEE. pp. 2217–2224
Yuan, J., Bae, E., Tai, X.C., 2010a · 2010
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A continuous max-flow approach to potts model, in: Computer Vision–ECCV 2010. Springer Berlin Heidelberg, pp. 379–392
Yuan, J., Bae, E., Tai, X.C., Boykov, Y., 2010b · 2010
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Towards an integrated crowdsourcing definition
Estellés-Arolas, E., González-Ladrón-De-Guevara, F., 2012 · 2012
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Microsoft coco: Common objects in context, in: Computer Vision–ECCV 2014. Springer, pp. 740–755
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Can masses of non-experts train highly accurate image classifiers?, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Springer International Publishing, pp. 438–445
Maier-Hein, L., Mersmann, S., Kondermann, D., Bodenstedt, S., Sanchez, A., Stock, C., Kenngott, H.G., Eisenmann, M., Speidel, S., 2014 · 2014
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Optimization-based interactive segmentation interface for multi-region problems, in: SPIE Medical Imaging, International Society for Optics and Photonics. pp. 94133T–94133T
Baxter, J.S., Rajchl, M., Peters, T.M., Chen, E.C., 2015 · 2015
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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, IEEE. pp. 1169–1176
Gurari, D., Theriault, D., Sameki, M., Isenberg, B., Pham, T.A., Purwada, A., Solski, P., Walker, M., Zhang, C., Wong, J.Y., et al., 2015 · 2015
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Biogames: A platform for crowd-sourced biomedical image analysis and telediagnosis
Mavandadi, S., Feng, S., Yu, F., Dimitrov, S., Yu, R., Ozcan, A., 2012 · 2012
Cited alongside, same era.
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., Summers, R.M., 2012 · 2012
Cited alongside, same era.
Fast interactive multi-region cardiac segmentation with linearly ordered labels, in: Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on, IEEE Conference Publications. pp. 1409–1412
Rajchl, M., Yuan, J., Ukwatta, E., Peters, T., 2012 · 2012
Cited alongside, same era.
Multi-organ abdominal ct segmentation using hierarchically weighted subject-specific atlases, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012. Springer Berlin Heidelberg, pp. 10–17
Wolz, R., Chu, C., Misawa, K., Mori, K., Rueckert, D., 2012 · 2012
Cited alongside, same era.
Design and evaluation of interactive proofreading tools for connectomics
Haehn, D., Beyer, J., Pfister, H., Knowles-Barley, S., Kasthuri, N., Lichtman, J., Roberts, M., 2014 · 2014
Cited alongside, same era.
Hierarchical max-flow segmentation framework for multi-atlas segmentation with kohonen self-organizing map based gaussian mixture modeling
Rajchl, M., Baxter, J.S., McLeod, A.J., Yuan, J., Qiu, W., Peters, T.M., Khan, A.R., 2016a
Cited in the paper.
Learning under distributed weak supervision
Rajchl, M., Lee, M.C., Schrans, F., Davidson, A., Passerat-Palmbach, J., Tarroni, G., Alansary, A., Oktay, O., Kainz, B., Rueckert, D., 2016b
Cited in the paper.
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Weakly-and semi-supervised learning of a dcnn for semantic image segmentation
Papandreou, G., Chen, L.C., Murphy, K., Yuille, A.L., 2015 · 2015
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Aggnet: Deep learning from crowds for mitosis detection in breast cancer histology images
Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., Navab, N., 2016 · 2016
Later among the works it cites.
Early experiences with crowdsourcing airway annotations in chest ct, in: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, Springer. pp. 209–218
Cheplygina, V., Perez-Rovira, A., Kuo, W., Tiddens, H.A., de Bruijne, M., 2016 · 2016
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Directed acyclic graph continuous max-flow image segmentation for unconstrained label orderings
Baxter, J.S., Rajchl, M., McLeod, A.J., Yuan, J., Peters, T.M., 2017 · 2017
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Multi-atlas segmentation using partially annotated data: Methods and annotation strategies
Koch, L.M., Rajchl, M., Bai, W., Baumgartner, C.F., Tong, T., Passerat-Palmbach, J., Aljabar, P., Rueckert, D., 2017 · 2017
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Deepcut: Object segmentation from bounding box annotations using convolutional neural networks
Rajchl, M., Lee, M.C., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M.A., Hajnal, J.V., Kainz, B., et al., 2017 · 2017
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