@inproceedings{5397f4cdca44442aa6d78c51cf16116f,
title = "Multi-test cervical cancer diagnosis with missing data estimation",
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.",
author = "Tao Xu and Xiaolei Huang and Edward Kim and Long, {L. Rodney} and Sameer Antani",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; SPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis ; Conference date: 22-02-2015 Through 25-02-2015",
year = "2015",
doi = "10.1117/12.2080871",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Hadjiiski, {Lubomir M.} and Tourassi, {Georgia D.}",
booktitle = "Medical Imaging 2015",
address = "United States",
}
