Abstract
Annotations delineating regions of interest can provide valuable information for training medical image classification and segmentation methods. However the process of obtaining annotations is tedious and time-consuming, especially for high-resolution volumetric images. In this paper we present a novel learning framework to reduce the requirement of manual annotations while achieving competitive classification performance. The approach is evaluated on a dataset with 59 3D optical projection tomography images of colorectal polyps. The results show that the proposed method can robustly infer patterns from partially annotated images with low computational cost.
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Li, W., Zhang, J., Zheng, WS., Coats, M., Carey, F.A., McKenna, S.J. (2013). Learning from Partially Annotated OPT Images by Contextual Relevance Ranking. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_54
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DOI: https://doi.org/10.1007/978-3-642-40760-4_54
Publisher Name: Springer, Berlin, Heidelberg
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