title: Learning from partially annotated OPT images by contextual relevance ranking.
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.
authors: CVIP, School of Computing, University of Dundee, Dundee, UK.
authors: Wenqi
authors: W
authors: Li
authors: CVIP, School of Computing, University of Dundee, Dundee, UK.
authors: Jianguo
authors: J
authors: Zhang
authors: School of Information Science and Technology, Sun Yat-sen University, China.
authors: Wei-Shi
authors: WS
authors: Zheng
authors: Ninewells Hospital & Medical School, University of Dundee, UK.
authors: Maria
authors: M
authors: Coats
authors: Ninewells Hospital & Medical School, University of Dundee, UK.
authors: Frank A
authors: FA
authors: Carey
authors: CVIP, School of Computing, University of Dundee, Dundee, UK.
authors: Stephen J
authors: SJ
authors: McKenna
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D041622:Tomography, Optical / Q000379:methods*