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Proceedings Paper

Multi-test cervical cancer diagnosis with missing data estimation
Author(s): Tao Xu; Xiaolei Huang; Edward Kim; L. Rodney Long; Sameer Antani
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Paper 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.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140X (20 March 2015); doi: 10.1117/12.2080871
Show Author Affiliations
Tao Xu, Lehigh Univ. (United States)
Xiaolei Huang, Lehigh Univ. (United States)
Edward Kim, Villanova Univ. (United States)
L. Rodney Long, National Library of Medicine (United States)
Sameer Antani, National Library of Medicine (United States)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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