Multi-test cervical cancer diagnosis with missing data estimation

Tao Xu, Xiaolei Huang, Edward Kim, L. Rodney Long, Sameer Antani

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

7 Scopus citations

Abstract

Cervical cancer is a leading most common type of cancer for women worldwide. Existing screening programs for cervical cancer suffer from low sensitivity. Using images of the cervix (cervigrams) as an aid in detecting pre-cancerous changes to the cervix has good potential to improve sensitivity and help reduce the number of cervical cancer cases. In this paper, we present a method that utilizes multi-modality information extracted from multiple tests of a patient's visit to classify the patient visit to be either low-risk or high-risk. Our algorithm integrates image features and text features to make a diagnosis. We also present two strategies to estimate the missing values in text features: Image Classifier Supervised Mean Imputation (ICSMI) and Image Classifier Supervised Linear Interpolation (ICSLI). We evaluate our method on a large medical dataset and compare it with several alternative approaches. The results show that the proposed method with ICSLI strategy achieves the best result of 83.03% specificity and 76.36% sensitivity. When higher specificity is desired, our method can achieve 90% specificity with 62.12% sensitivity.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2015
Subtitle of host publicationComputer-Aided Diagnosis
EditorsLubomir M. Hadjiiski, Georgia D. Tourassi
PublisherSPIE
ISBN (Electronic)9781628415049
DOIs
StatePublished - Jan 1 2015
EventSPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis - Orlando, United States
Duration: Feb 22 2015Feb 25 2015

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9414
ISSN (Print)1605-7422

Conference

ConferenceSPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis
CountryUnited States
CityOrlando
Period2/22/152/25/15

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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    Xu, T., Huang, X., Kim, E., Long, L. R., & Antani, S. (2015). Multi-test cervical cancer diagnosis with missing data estimation. In L. M. Hadjiiski, & G. D. Tourassi (Eds.), Medical Imaging 2015: Computer-Aided Diagnosis [94140X] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9414). SPIE. https://doi.org/10.1117/12.2080871