A new image data set and benchmark for cervical dysplasia classification evaluation

Tao Xu, Cheng Xin, L. Rodney Long, Sameer Antani, Zhiyun Xue, Edward Kim, Xiaolei Huang

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

10 Scopus citations

Abstract

Cervical cancer is one of the most common types of cancer in women worldwide. Most deaths of cervical cancer occur in less developed areas of the world. In this work, we introduce a new image dataset along with ground truth diagnosis for evaluating image-based cervical disease classification algorithms. We collect a large number of cervigram images from a database provided by the US National Cancer Institute. From these images, we extract three types of complementary image features, including Pyramid histogram in L*A*B* color space (PLAB), Pyramid Histogram of Oriented Gradients (PHOG), and Pyramid histogram of Local Binary Patterns (PLBP). PLAB captures color information, PHOG encodes edges and gradient information, and PLBP extracts texture information. Using these features, we run seven classic machine-learning algorithms to differentiate images of high-risk patient visits from those of low-risk patient visits. Extensive experiments are conducted on both balanced and imbalanced subsets of the data to compare the seven classifiers. These results can serve as a baseline for future research in cervical dysplasia classification using images. The image based classifiers also outperform results of several other screening tests on the same datasets.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
EditorsLuping Zhou, Yinghuan Shi, Li Wang, Qian Wang
PublisherSpringer Verlag
Pages26-35
Number of pages10
ISBN (Print)9783319248875
DOIs
StatePublished - 2015
Event6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 5 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9352
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
Country/TerritoryGermany
CityMunich
Period10/5/1510/5/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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