Learning from partially annotated OPT images by contextual relevance ranking

Wenqi Li, Jianguo Zhang, Wei-Shi Zheng, Maria Coats, Frank A. Carey, Stephen J. McKenna

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    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.
    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2013
    Subtitle of host publication16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part III
    EditorsKensaku Mori, Ichiro Sakuma, Yoshinobu Sato, Christian Barillot, Nassir Navab
    Place of PublicationBerlin
    PublisherSpringer
    Pages429-436
    Number of pages8
    ISBN (Electronic)9783642407604
    ISBN (Print)9783642407598
    DOIs
    Publication statusPublished - 2013
    Event16th International Conference on Medical Image Computing and Computer Assisted Intervention - Toyoda Auditorium, Nagoya University, Nagoya, Japan
    Duration: 22 Sep 201326 Sep 2013
    http://www.miccai2013.org/index.html

    Publication series

    NameLecture notes in computer science
    PublisherSpringer
    Volume8151
    ISSN (Print)0302-9743

    Conference

    Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention
    Abbreviated titleMICCAI 2013
    CountryJapan
    CityNagoya
    Period22/09/1326/09/13
    Internet address

    Keywords

    • Algorithms
    • Artificial Intelligence
    • Colonic Polyps
    • Humans
    • Image Enhancement
    • Image Interpretation, Computer-Assisted
    • Microscopy
    • Pattern Recognition, Automated
    • Reproducibility of Results
    • Sensitivity and Specificity
    • Tomography, Optical

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  • Student Theses

    Analysis of Colorectal Polyps in Optical Projection Tomography

    Author: Li, W., 2015

    Supervisor: Zhang, J. (Supervisor) & McKenna, S. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy

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    Cite this

    Li, W., Zhang, J., Zheng, W-S., Coats, M., Carey, F. A., & McKenna, S. J. (2013). Learning from partially annotated OPT images by contextual relevance ranking. In K. Mori, I. Sakuma, Y. Sato, C. Barillot, & N. Navab (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part III (pp. 429-436). (Lecture notes in computer science; Vol. 8151). Springer . https://doi.org/10.1007/978-3-642-40760-4_54