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Omni-Supervised Learning: Scaling Up to Large Unlabelled Medical Datasets

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11070)


Two major bottlenecks in increasing algorithmic performance in the field of medical imaging analysis are the typically limited size of datasets and the shortage of expert labels for large datasets. This paper investigates approaches to overcome the latter via omni-supervised learning: a special case of semi-supervised learning. Our approach seeks to exploit a small annotated dataset and iteratively increase model performance by scaling up to refine the model using a large set of unlabelled data. By fusing predictions of perturbed inputs, the method generates new training annotations without human intervention. We demonstrate the effectiveness of the proposed framework to localize multiple structures in a 3D US dataset of 4044 fetal brain volumes with an initial expert annotation of just 200 volumes (5% in total) in training. Results show that structure localization error was reduced from 2.07 ± 1.65 mm to 1.76 ± 1.35 mm on the hold-out validation set.

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  • DOI: 10.1007/978-3-030-00928-1_65
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We acknowledge the Intergrowth-\(21^{st}\) study [10] for the image datasets. This work was supported by the National Institutes of Health (NIH) through National Institute on Alcohol Abuse and Alcoholism (NIAAA) (2 U01 AA014809-14), the Royal Academy of Engineering under the Engineering for Development Research Fellowship Scheme, and the EPSRC Programme Grant Seebibyte (EP/M013774/1).

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Correspondence to Ruobing Huang .

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Huang, R., Noble, J.A., Namburete, A.I.L. (2018). Omni-Supervised Learning: Scaling Up to Large Unlabelled Medical Datasets. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science(), vol 11070. Springer, Cham.

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